I S K O

edited by Birger Hjørland and Claudio Gnoli

 

Science

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Table of contents:
[Part I: Basic conceptions of science and the scientific method]
1. Etymology and near synonyms
2. Overall developments in the conception of science
3. The scientific method
    3.1 Rationalism
    3.2 Empiricism
    3.3 Historicism
    3.4 Pragmatism (with Marxism, critical theory and feminist epistemology)
    3.5 Conclusion on method
[Part II: The study of science]
4. The study of science
    4.1 The philosophy of science: 4.1.1 Metaphysical issues; 4.1.2 The Demarcation problem; 4.1.3 The classification of the sciences
    4.2 The history of science
    4.3 The sociology of science and science studies: 4.3.1 Merton; 4.3.2 Social constructivism (4.3.2.1 Introduction; 4.3.2.2 The Strong Program; 4.3.2.3 Bruno Latour and actor-network theory); 4.3.3 Conclusion on sociology of science
    4.4 Other fields studying science: 4.4.1 Scientometrics; 4.4.2 Cognitive science of science and psychology of science; 4.4.3 Information science; 4.4.4 Terminology studies; 4.4.5 Genre studies
    4.5 Conclusion of Section 4
5. Scholarly communication and knowledge organization
    5.1 Overview
    5.2 Information technology’s (IT) influences on science
    5.3 Do “data” displace academic documents?
    5.4 Conclusion: Epistemology as the basis for studying scholarly communication
[Part III: Further developments in the concept of science]
6. Further developments in the concept of science
    6.1 Introduction
    6.2 “Entrepreneurial science” and “triple helix”
    6.3 “Mode 2 research”
    6.4 “Technoscience”, “postmodern science” and “postnormal science”
    6.5 Citizen science (“science 2.0”)
    6.6 Big science
    6.7 Conclusion of Section 6.
7. General conclusion
Acknowledgments
Endnotes
References
Appendix 1: A Marxist Understanding of Science (Hörnig 1985)
Colophon

Abstract:
While almost everybody can understand and use the word science, a deeper examination of the concept reveals deep difficulties. Can science, for example, be defined by the application of a universal scientific method? Are there clear criteria, that can discriminate science from pseudoscience and non-science? The article discusses these questions as well as the following: How have conceptions of science developed historically? What are the most important tensions in different conceptualizations of science? Which fields are studying science, and what are the roles of information science and knowledge organization in the study of science? How should “science” be understood or constructed as an object of research in these fields?

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1. Etymology and near-synonyms

The English word science comes, via French, from Latin scire (know) and scientia (knowledge). Oxford English Dictionary (2021) (OED) documents 10 senses (17 including sub-senses), some of which are obsolete, and others are special senses, which can be ignored for the purpose of this article [1]. Based on the resting senses, science may thus accordingly to OED mean (4a) a field of study, a → discipline, or an activity concerned with theory, or (4b) a “branch of study that deals with a connected body of demonstrated truths or with observed facts systematically classified and more or less comprehended by general laws, and incorporating trustworthy methods”. It may also mean (5a) “The kind of organized knowledge or intellectual activity of which the various branches of learning are examples” (e.g., what is taught at universities). It is often implicit understood (5b) to be concerned only with the physical universe (the most usual sense of science since the mid-19th century) and contrasted with religion when regarded as constituting an influence on a person's world view or belief system. It is also used (5c) about the scientific principles or processes which govern or underpin a (specified) phenomenon or technology and (5d) about scientific results obtained from observations, experiments, etc.

We see that etymologically the link between the words science and knowledge have been very close and that the word science is both used about a kind of knowledge (e.g., as the product of inquiry) and about the kind of social organization that produces that knowledge. We also see that science is associated with methods assumed to provide true knowledge. This means, that the conception of science itself is deeply influenced by epistemological issues on how to obtain knowledge. It is relatively difficult in the scholarly literature to find articles discussing different conceptions of science [2]. Below such different conceptions are in the focus and are presented to the extent they have been identified by the author. It is shown how developments in the theory of knowledge have made it increasingly difficult to define the term science in epistemological terms.

What today is termed science (and scientist) was formerly mostly termed philosophy (and philosopher). William Whewell (1834) coined the term scientist to describe someone who studies the structure and behavior of the physical and natural world through observation and experiment. Formerly science as we understand the term today was mostly called “natural philosophy” [3] (and rather clearly distinguished from “natural history” [4] and from chemistry [5]). Persons considered “philosophers” in the history of philosophy were usually also (or even primarily) great scientists, and, retrospectively, we apply the term scientist to persons such as Isaac Newton and Charles Darwin, and science to the work done by them. A valuable examination of the historical development of the relation between the words science and philosophy and their changing meanings was done by Ross (1990), who concluded (814):

Despite the antiquity of the terms ‘science’ and ‘philosophy’, they acquired their present meanings only during the nineteenth century. With the benefit of hindsight, we can find much activity which we would call ‘scientific’ or ‘philosophical’ in earlier periods; but the respective practitioners did not see themselves as divided into distinct camps, or at least not in a way we would recognise today. Generally, much of what we now call philosophy was tacitly accepted as a proper part of science (or 'philosophy' as it was then called), but contrasted with the useless 'school metaphysics' of the universities. It was only when philosophy more or less as we now know it became a university specialism in the nineteenth century that it became possible for scientists to leave the more philosophical aspects of science to specialist philosophers”. [6]

As already stated, the main meaning of the English science since the mid-19th century is narrowed to natural science (sense 5b in OED), excluding, for example, the humanities. According to Daston (2015, 241) this narrowing was influenced by Auguste Comte’s (1830-42) hierarchy of sciences, in which only some fields had reached the stage of “positive knowledge” [7]. The corresponding word to science has somewhat different meanings in other languages. The German word Wissenschaft, for example, includes the humanities and philosophy (and has thus not followed the same influence from Comte). This limited meaning of the English word is, however, often a problem also for English language authors, who then choose to use the English word in the wider sense [8]. An example is Hoyningen-Huene (2013, 8-9), who wrote:

First, with respect to disciplines covered by the term ‘science’, I want to understand the term and thus the question ‘What is science?’ in their broadest possible sense. Therefore, not only all sciences in the (English) standard sense shall be included, namely the natural sciences, but also mathematics, the social sciences, the humanities, and the theoretical parts of the arts. Unfortunately for my project, there is no appropriate single English term denoting this broad variety of disciplines. We might collectively refer to them as ‘research fields’ or ‘research disciplines’. In German, there is the term “Wissenschaft”, which covers all research fields that I intend to cover here. However, for lack of a better word, the term ‘science’ will be subsequently used, although it does not represent well the semantic shift proposed here. Other authors pursuing studies of a similar breadth and being confronted with the same difficulty have also resorted to the very broad usage of the term ‘science’.

Likewise, in phrases such as “classification of sciences”, “science mapping” and “atlas of science”, the word science is often understood in its broad meaning, corresponding to Wissenschaft. The platform Web of Science also includes arts and humanities in its coverage, as does the book The Cambridge History of Science (Lindberg and Numbers 2002-2020). This broad meaning will also be used here in ISKO Encyclopedia of Knowledge Organization (IEKO) unless otherwise specified. The term scientific communication may also be understood in this broad way (e.g., by Ossenblok 2016), although in this case the term scholarly communication (or scientific and scholarly communication) may be preferred in order explicitly to include the humanities. The term scholar includes, according to the OED any “person who is highly educated and knowledgeable, usually as a result of studying at a university” (this term is, however, also ambiguous in that it sometimes includes natural scientists and sometimes is used only about people in the humanities). The discussion of the issue what is included in the term (e.g., whether the humanities should fall within the label “science”) is in philosophy called “the demarcation problem” and we return to this in Section 4.1.2, but it should be said here, that some authors, e.g., Mahner (2007) explicitly consider the humanities to be “non-science” fields [9].

“Science” is used both as a genus term for all sciences (whether understood in the narrow or in the broad sense), and about a specific science (“scientific discipline” or just “discipline”). This generic use seems connected to the idea of the “unity of science”. The use of “science” about different fields of knowledge is often relative to a positivist or non-positivist understanding (e.g., in history [10] and psychology [11]). The most famous attempt to describe the difference between science and humanities was the distinction between explanation (German: erklären) in the (natural) sciences and understanding (German: verstehen) in the humanities/human sciences (or the older German term Geisteswissenschaften) suggested by Droysen ([1858], 1937) and later used by Dilthey (1894, 1314). This understanding has, however, been criticized by modern hermeneutics (see, e.g., Caputo 2018). An important trend today is to conceive both sciences and humanities to be about interpretations, to consider positivism a failed idea, and to emphasize the concept “culture” as important for both natural and human sciences (cf., Margolis 2009) [12]. Rather than upholding a sharp dualism between “science” and “humanities”, the contemporary tendency is to acknowledge the uniqueness of many forms for studies. Still, however, some researchers do not feel that “science” is a proper label for their field, but prefer other labels, e.g., “studies”. “Science” and “studies” may be considered synonyms in some fields (or at least hard to distinguish as in “information science” versus “information studies”), but in Web of Science, for example, there are two different categories environmental sciences and environmental studies [13], indicating an operational criterion of differentiation between the two concepts. Thus, the application of the term science to a field of study seems not to be a closed, but an open issue.

Research as a noun has according to OED four meanings of which 2a is synonym with science in the broad sense:

Systematic investigation or inquiry aimed at contributing to knowledge of a theory, topic, etc., by careful consideration, observation, or study of a subject. In later use also: original critical or scientific investigation carried out under the auspices of an academic or other institution.

However, “research” is often used in a yet broader sense, including, for example, journalist research.

Another near synonym is inquiry, which is defined by the Oxford English Dictionary (meaning 1.a): “The action of seeking, esp. (now always) for truth, knowledge, or information concerning something; search, research, investigation, examination”. The term inquiry is, as described in Section 3.4, a preferred term by many pragmatic philosophers.

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2. Some developments in the conception of science

There is today no consensus of what the term science means. Ziman (2000, 12) holds that: “science is too diverse, too protean, to be captured in full by a definition”. Sadegh-Zadeh (2015, 856) expressed the same opinion:

It might come as a surprise that there is as yet no agreement on what science is. Although attempts to characterize or even to define it have a long history, going all the way back to Aristotle, it was not until the British empiricism of the seventeenth and eighteenth centuries that definite criteria were suggested for differentiating science from non-science and pseudoscience. […]. Science is too complex a phenomenon to be characterized by a single, simple demarcation criterion. A multicriterial concept of science will be necessary to capture its degree of complexity”. [14]

Achinstein (2011, 346) found that many people consider scientific inquiry as superior to other forms of inquiry and subscribing to three principal theses:

  1. Science aims at and can achieve knowledge of the world.
  2. Knowledge in science requires proof, the standards for which are universal for all the sciences, and can be formulated in a set of rules called the ‘scientific method’.
  3. Although unproved propositions (sometimes called ‘hypotheses’) are introduced into science for purposes of investigation, scientists are not justified in believing them until they are proved in accordance with the standards set by the ‘scientific method’.

Achinstein’s quote, as a to-day’s view of science, is partly challenged by Hoyningen-Huene (2013, 1-6), who — with many reservations — distinguished four phases in the history of the conceptions of science:

  1. The first phase started around the times of Plato (about 428–348 BC) and Aristotle (384–322 BC) and dominated Western antiquity and the Middle Ages and ended in the early seventeenth century. Two traits for scientific knowledge are postulated for this period: (1) The epistemic ideal of the absolute certainty of knowledge. Scientific knowledge conceived in this manner, or with the Greek word, episteme, stands in sharp contrast to mere belief, or doxa. Only episteme, by being certain, qualifies as scientific. (2) The methodological idea of deductive proof as the appropriate means to realize this ideal. Euclidean geometry was understood as a model, which could in principle be applied to all areas of science.
  2. The second phase in Hoyningen-Huene’s schematic history of philosophy of science begins in the early Seventeenth century and ends sometime in the second half of the Nineteenth century. This phase continues the first phase by equally subscribing to the epistemic ideal of the certainty of scientific knowledge. However, it is discontinuous regarding how this ideal is to be achieved. Whereas in the first phase, only deductive proof is a legitimate means to attain the certainty of knowledge, the second phase liberalizes this requirement to what will eventually be known as the “scientific method”. What is meant exactly by that concept is typically left unanswered and could be understood either as one single method or as a set of methods. Deductive proof is still a part of the scientific method, but the most important extension concerns inductive procedures. Hoyningen-Huene mentions Galileo Galilei (1564–1642), Francis Bacon (1561–1626), René Descartes (1596–1650), and (a little later), Isaac Newton (1642–1727) as the most famous protagonists of “the scientific method”.
  3. The third phase begins in the late Nineteenth century and stretches into the last third of the Twentieth century. This phase is characterized with the belief that scientific knowledge is not certain and never can be certain, but it is hypothetical and fallible. Both inductivist and deductivist philosophies of science, though relying on strict methodological procedures for confirmation or testing of hypotheses, stress the hypothetical nature of scientific knowledge from the natural sciences.
    This is true both with respect to the mathematical, the natural, and the human sciences. “For the mathematical sciences, the discovery of non-Euclidean geometries in the course of the nineteenth century is dramatic. It demonstrates that the belief in the uniqueness of Euclidean geometry, and thus the conviction of its unconditional truth, is unfounded. However, the conclusiveness of mathematics is restored if the axioms of any mathematical theory are taken as assumptions whose truth or falsehood is not up for grabs […]. In the natural sciences, the process of erosion of scientific certainty is often only associated with the advent of the special theory of relativity [Einstein 1905] and of quantum mechanics [Born 1924] [15] and others]” (4).
  4. The fourth phase begins during the last third of the Twentieth century and continues until today. In this phase, belief in the existence of scientific methods conceived of as strict rules of procedure has eroded. “Historical and philosophical studies have made it highly plausible that scientific methods with the characteristics posited in the second or third phases simply do not exist. Research situations, i.e., specific research problems in their specific contexts, are so immensely different from each other across the whole range of the sciences and across time that it appears utterly impossible to come up with some set of universally valid methodological rules” (4-5).

Hoyningen-Huene (2013, 6) further wrote:

Thus, the first two phases are connected by the ideal of certainty for scientific knowledge, but deductive proof is replaced by scientific method(s) in the second phase. The second and third phases are connected by the idea of scientific method(s), but the ideal of certainty is replaced by fallibility in the third phase. The third and fourth phases are connected by the idea of the fallibility of scientific knowledge, but in the fourth phase, the belief in scientific method(s) as constitutive for science ceases. Note that only in the present fourth phase, the question about the nature of science becomes dramatic, because the only feature left for science, namely fallibility, is by no means a sign for its uniqueness. Therefore, it is no exaggeration to state that although we are familiar today with the phenomenon of science to a historically unparalleled degree, we do not really know what science is.

These quotes by Achinstein and Hoyningen-Huene made it clear that the conceptions of science have been intricately connected to ideas about the scientific method, to which we now turn.

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3. Scientific method

Some philosophers and scientists defend the view that there exist basic methods common to all sciences [16]. Kincaid (1998) argued that one of the basic characteristics of positivism is the idea that there is a universal and a priori scientific method. However, as presented in Section 2, the tendency today is, with Hoyningen-Huene (2013), that former times belief in the existence of scientific methods conceived of as strict rules of procedure has eroded. Bauer (1992, vii) agreed, writing against using “sweeping generalizations” about science [17].

Achinstein (2011, 346-50) found that the three principal theses of scientific inquiry have been defended by three prominent but very different historical views: (1) Cartesian rationalism (2) Newton’s and Mill’s inductivism and (3) William Whewell’s inference to the best explanation [18], after which he presented some contemporary positions that contradict one or more of these three ideas, but does not come up with a view of positive characteristics of science or alternatives to the contradicted positions. In the following some of Achinstein’s arguments will be inscribed in the presentation of the following positions: (a) Rationalism with deductionism (b) Empiricism with inductivism (c) Historicism with Kuhn’s philosophy of science and (d) Pragmatism with feminist epistemology, Marxism and critical theory. These four positions represent a classification of positions suggested by Hjørland (1998), but are related to commonly used labels and positions in epistemology. Rationalism and empiricism are mostly considered the basic theories of knowledge during the Enlightenment that were combined by the logical positivists in the beginning of the 20th century but were criticized by Kuhn’s (1962) historicism. Pragmatism, which was founded by Peirce (1878), is related to critical theory and to feminist standpoint epistemology and seems to have growing influence today. The positions are further described in independent sections below, but a preliminary characterization of them is:

  • Rationalism gives priority to a priori thinking. Research in this tradition (e.g., → logical division in classification) is characterized by its lack of a described empirical methodology (although empirical knowledge may be implicitly given). In this view, our knowledge, for example, that 2+2=4, is considered based on a form of intuition or the direct, rational apprehension of its truth (cf. Barnes, Bloor and Henry 1996, 173).
  • Empiricism gives priority to the collection, description, and processing of → data in a neutral way (i.e., not data selected by theoretical criteria). With enough data, a hypothesis may be considered verified, and empiricism may be understood as the ideal of letting data speak for themselves.
  • Historicism gives priority to the interpretation of data in the light of research tradition and “paradigm” (realizing that the collection, description, and processing of data is influenced by research contexts).
  • Pragmatism emphasizes the analysis of the purposes, consequences, and the interests, which the knowledge/research is supporting, and shares many characteristics with historicism. In this view, our knowledge that 2+2=4 can never be finally proven by any rationalist or empiricist method but is based on its broad utility for organizing practical affairs (see further about the sociological analysis of the 2+2=4 example in Barnes, Bloor and Henry 1996, chapter 7).

A classification such as the one provided here is not true or false but may be more or less fruitful for certain purposes, for example, for considering positions in the conceptions of science (the formerly mentioned use of deductive and inductive methods in the characterization of two phases of the conception of science by Hoyningen-Huene seems partly to confirm its fruitfulness for this purpose). The classification may also be fruitful to classify contemporary positions, e.g., Hjørland’s (2013b) classification of positions in → knowledge organization. An obvious drawback is that it hides the complexities and mutual dependencies of theories of knowledge and that it simplifies them in a way that philosophers and scientists may have difficulties to recognize as their own standpoints. It is also important to realize that each of these labels (e.g., empiricism) is highly ambiguous, and that the classification itself implies an interpretation of them [19].

There is another issue with the suggested classification that needs to be considered. One of the anonymous reviewers of this paper wrote: “Rationalism and empiricism are described as extreme positions that no sane person would support them in modern time”. Yes, these positions (and their combination in logical positivism) are often declared dead, but as Bentz and Shapiro (1998, 26-35) wrote in the section “The mysterious death and afterlife of positivism”, in spite of its official death (and its replacement with other labels such as “post-positivism”), positivism, explicitly or implicitly, is at the core of the modern worldview of scientific, technological, bureaucratic, commercial civilization. The authors support their view (30-31) on Habermas’ (1971) view “that, at the root, positivism is simply the denial of reflection, that is, of the need to reflect explicitly on the philosophical and social conditions of knowledge”.

The classification in these four positions is based on studies of these philosophical traditions and on the task of classifying contemporary epistemological approaches, for example, in knowledge organization, where they have been found it to work very well (e.g., Hjørland 2011b; 2013b). In relation to Hoyningen-Huene’s (2013) and Achinstein’s (2011) classification of views of science only two of these four positions are clearly visible in what is referred from both authors (although their writings may perhaps also be interpreted to involve historicism and pragmaticism). Our suggestion is that historicism and pragmaticism represent answers to the crisis of logical positivism, and that these positions are social epistemological positions opposed to the individualist epistemological positions of rationalism and empiricism. Rationalism and empiricism are here, as by Achinstein, understood as defending the principal thesis that knowledge has an absolute certain basis (cf. Sosa 1998) [20]. Historicism and pragmatism are understood as fallibilist, considering all knowledge claims to be open to challenge, revision, correction, or rejection (not to be confused with epistemological skepticism).

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3.1 Rationalism

We saw that the first stage in the conception of science by Hoyningen-Huene (2013, 1-6) emphasized the ideal of the absolute certainty of knowledge and, second, the methodological idea of deductive proof as the appropriate means to realize this ideal. Rationalism mostly took mathematics and geometry as models for all science. The idea is that all knowledge must be deduced from elements of basic truth, which are apodictically certain. They are based on intuitions of what must necessarily be the truth. Main representatives of rationalism include Plato and René Descartes [21].

Achinstein (2011) found that rationalism (in the version developed by Descartes [22]) has been one of the main arguments for three principal theses about science [23]. He describes (347) how Descartes considered these rules to be applicable to the sciences generally, not just to mathematics and he deduced three “laws of nature”, the first two of which yield what came to be known as the law of inertia — that moving bodies if left to themselves tend to continue to move in straight lines.

Hoyningen-Huene (2013, 152) found that rationalism is a problematic position: “Neither in mathematics nor in the natural sciences, let alone the other areas of learning, do we believe any longer in the attainability of any sort of immediate certainty” [24]. However, despite this criticism, rationalism is an important impulse, even in modern science (including knowledge organization [25]). An example is Chomsky’s linguistics, which explicitly acknowledged Descartes’ rationalism. Some versions of → ontologies as knowledge organization systems likewise seem based on rationalist assumptions. In medicine rationalism and empiricism are used, on what seems to be important positions which are both related to empirical research: Rationalists emphasize the importance of empirical investigation into basic mechanisms of disease, whereas empiricists are interested in whether something works, regardless of causes or mechanisms [26]. Buhr and Starke (1985) [27] found that rationalism has an important core, but that its principles have been generalized in problematic ways. Further works on rationalism include Nelson (2005), Fraenkel, Perinetti and Smith (2011) and Boghossian and Williamson (2020).

Popper’s “critical rationalism” (Popper [1934], 1959; 1963) deserves to be presented. It is rationalist in its emphasis on deductive methods and skepticism towards inductive methods. Popper found that a scientific theory can never be verified because no amount of empirical evidence will ever suffice to prove a theory as contrary evidence might always be found be later research. Popper is therefore a fallibilist philosopher (and in this respect deviates from the former characterization of rationalism). His methodology is the hypothetico-deductive model, according to which research starts from a hypothesis (conjecture), the consequences of which are then deduced. An observation (e.g., an experiment) is then made to see if the deduced consequences fit with the empirical observation. If not, the hypothesis (conjecture or theory) is falsified. Thus, according to Popper, while no amount of experimentation can ever prove a theory right, a single observation or experiment may prove it wrong. For Popper, the characteristics of something deserving the label ”science” is that it is formulated in a precise way, that allows it to be falsified. This is also a demand that all scientific concepts are well-defined throughout a research process. As no scientific claim or theory is ever finally verified, the best knowledge is the one that has resisted attempts from the scientific community to falsify it.

Popper’s view has been discussed and criticized, including by Kuhn (1962) and by Popper’s professed disciple, Lakatos. Lakatos (1976), here cited from Musgrave and Pigden (2016, §2.1), argued that mathematical concepts were end-points rather than starting points in a dialectical process “in which the constituent concepts are initially ill-defined, open-ended or ambiguous but become sharper and more precise in the context of a protracted debate. The proofs are refined in conjunction with the concepts (hence “proof-generated concepts”) whilst “refutations” in the form of counterexamples play a prominent part in the process. Lakatos disagreed with Popper that a single experiment can falsify a theory [28]. Thereby Lakatos showed that both the verification and the falsification of theories cannot be made disregarding the conceptual and historical context. Concepts are not “given” as clear-cut understandings but may gain clarity by the research process.

Lakatos’ criticism is not just relevant about Popper’s version, but as a criticism of rationalism overall, and it points towards the historicist position (see Section 3.3). The same can be said of Lakatos’ (1976) critique of formalism, logicism and intuitionism in the philosophy of mathematics.

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3.2 Empiricism

The word empiricism is difficult: What we consider to be the core in its historical development, the British empiricists, did not consider themselves as empiricists, but said explicitly that they were not (cf. Fraassen 2002, 32). He also wrote (xiii) that all the philosophers we count as empiricist rejected the positions of their predecessors. The term is today generally used about the view that knowledge about the physical world is possible only through observation and experiment, not through intuitions, which may be unreliable and therefore cannot be used as basis for deductions. The rationalist belief in intuitions as a scientific method was thus rejected by empiricists. Hoyningen-Huene (2013, 161-2) wrote:

Regarding this topic, logical empiricists continued the inductivist tradition that goes back at least to the beginning of modern natural science in the seventeenth century. This tradition believes, in some variant or other, that there are procedures that justify the generalization of empirical data to general hypotheses; the core of these procedures is a “principle of induction.

Nickles (2005) wrote that ”in the twenty-first century nearly everyone is an empiricist in the everyday sense of taking experience seriously as a basis for knowledge claims about the natural world and human behavior, but most philosophers reject traditional, doctrinaire empiricism — the view that human sense experience provides a special connection of the knowing mind to the world and thus provides a foundation on which knowledge can build, step by step”. Nickles listed a range of challenges which changed or ousted classical empiricism [29]. Already the classical rationalists [30] and the founder of the phenomenological tradition, Edmund Husserl, among others, considered empiricism to be a self-refuting position [31].

Few, if any, people today would claim that science can do without empirical studies (and thus adhere to “empiricism” in one sense of the term). However, studies can be done more or less “blindly”, or in theory-informed ways. In the classification of positions used in this article, empiricism is understood as one ideal of doing empirical studies, that contrasts with other positions of doing empirical studies, discussed in sections 3.3 and 3.4.

It is a widely held view that logical empiricism (and logical positivism and empiricism generally) run into serious troubles at the time when Kuhn (1962) published his book. A basic argument by Kuhn (formerly expressed by Duhem [1906] 1991, Feyerabend 1957, Hanson 1958, and others) is that observations are “theory-laden”, which means that there is no clear boarder between observations and theory, and we therefore must view knowledge claims in their theoretical and historical-cultural contexts. As Fleck ([1935] 1979, 38) [32] wrote:

cognition must not be construed as only a dual relationship between the knowing subject and the object to be known. The existing fund of knowledge must be a third partner in this relation as a basic factor of all new knowledge. […] What is already known influences the particular method of cognition, and cognition, in turn, enlarges, reviews, and gives fresh meaning to what is already known. Cognition is therefore not an individual process of any theoretical ‘particular consciousness’. Rather it is the result of a social activity, since the existing stock of knowledge exceeds the range available to any one individual.

Therefore, the main problem with empiricism is that it does not consider how the observer is influenced by his or her background assumptions [33]. To take this into consideration in scientific methodology requires an alternative perspective (historicism) to which we turn in Section 3.3.

A clear example of empiricism in modern biology is numerical taxonomy (or “phenetics”), which was developed by Sokal and Sneath (1963) [34]. This is an approach claiming to be based solely on observable, measurable similarities and differences of the things to be classified. → Classification is based on overall similarity: The elements that are most alike in most attributes are classified together. As many characteristics as possible of a set of organisms are described and represented in a database, and classifications are constructed by statistical calculations of correlations. The characteristics must not be chosen from theoretical principles of which are most important, because this introduces an element of subjectivism not approved by empiricism. (Sokal and Sneath had to admit, however, that even numerical taxonomy is unable to eliminate subjectivity in classification [35]). Numerical taxonomy conflicts with an alternative empirical methodology for classification suggested by Charles Darwin (1859), which will be presented in Section 3.3.

Some recent defenses of a modified empiricism are Fraassen (1980 [36]; 2002), Aune (2009) [37] and Johansson (2021) [38].

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3.3 Historicism (with Kuhn’s philosophy)

Historicism is an old tradition in philosophy, mainly developed in Germany in the Nineteenth century. Its main characteristics is an insistence on the historicity of all knowledge and cognition. Two dimensions should be considered: the history of the object and of the subject. The historicity of the object is the view that the world is in constant development, and this development is important for science to map. The historicity of the subject can be illustrated by generalizing a quote from Edwards (2010, xvii; here modified) [39]:

Our perspective on the world keeps changing, for many reasons. Scholars and scientists argue about how to interpret the evidence, finding flaws in earlier interpretations. And we, the researchers, keep changing. What we want to know about the world, what we hope to discover there, the concepts and instruments we use, depends on who we are now.

And from Mazzocchi (2015, 1253):

Scientific research does not take place in a purely theoretical and rational environment of facts, experiments and numbers. It is carried out by human beings whose cognitive stance has been formed by many years of incorporating and developing cultural, social, rational, disciplinary ideas, preconceptions and values, together with practical knowledge. Scientists form their ideas and hypotheses based on specific theoretical and disciplinary backgrounds, which again are the result of decades or even centuries of history of scientific and philosophical thought. [40]

Historicism influenced the philosophy of science in the 20th century mainly by Kuhn’s (1962) book The Structure of Scientific Revolutions, which was an attack on “positivism” as Kuhn understood the term. The most important aspect of this book was probably the rejection of the positivist idea of what Kuhn labels “incrementalism”, that there is a continuous accumulation of an ever-increasing stock of truths. What science gets correct once, stays correct forever, it does not develop theories in conflict with former theories. Nickles (2017) wrote:

Many scientists, philosophers, and laypersons have regarded science as the one human enterprise that successfully escapes the contingencies of history to establish eternal truths about the universe, via a special, rational method of inquiry. Historicists oppose this view. In the 1960s several historically informed philosophers of science challenged the then-dominant accounts of scientific method advanced by the Popperians and the positivists (the logical positivists and logical empiricists) for failing to fit historical scientific practice and failing particularly to account for deep scientific change.

As we saw in Section 3.2, Kuhn considered observations to be “theory-laden” and refused the individual epistemologies of rationalism and empiricism. Kuhn is famous for introducing the concepts “paradigm” and “paradigm shift” (although they are unclear and have been criticized) [41]. The idea is that the single scientist is trained and socialized in a scientific tradition, and this socialization influences the way he or she looks at the world (or more precisely at the specific scientific discipline and the specific research problems with which she works). The socialization is not just verbal, but also influenced by tacit or implicit knowledge, for example, by doing experiments in a laboratory. A paradigm influences the research questions asked, the methods used, what counts as proper results etc.

We can illustrate this with the different methodological ideals in numerical taxonomy (described in Section 3.2) versus the paradigm founded by Charles Darwin. Darwin’s main contribution to classification was not just his view “all true classification is → genealogical” (Darwin 1859, 420), but rather his methodology for operationalizing classification based on this principle. Darwin realized that he needed to decide which traits to use in classification, and why. Richards (2016, 90-92) explains:

One of the main advantages of Darwin’s theoretical approach is that, unlike previous approaches, it gave operational guidance. Those shared characters or traits that indicate common ancestry, by virtue of inheritance from a common ancestor, should be used to classify. Those that do not indicate common ancestry are irrelevant. To make this distinction between the characters or traits that indicate ancestry from those that do not, Darwin adopted the terms ‘homology’ and ‘analogy’ [and further developed these concepts]. [42]

Darwin’s methodological principles are thus deeply connected to his theoretical view on biological evolution, which introduces an element of subjectivity, which empiricism opposes [43]. Therefore, the fundamental difference between empiricism and historicism, as understood here, is the former’s ideal of selecting characteristics by disregarding theoretical criteria of relevance, while historicism acknowledges the role of the researcher’s theoretical positions, that different methodologies are not neutral, but have to be worked out as a part of the theoretical development of the field. Whereas rationalism and empiricism only consider the attributes of things to be classified, historicism also makes theories and traditions important for classification. Historicism considers the attributes of things in addition to consider who have made/constructed/selected the attributions, and how different cultures, traditions and paradigms are considering different attributes.

Concerning research methodology, Mallery, Hurwitz and Duffy (1992) as well as Heelan (1997), D’Agostino (2015) and Hoyningen-Huene and Lohse (2015, 136) interpreted Kuhn’s paradigms as analogous to Hans-Georg Gadamer's notion of a linguistically encoded social tradition, and thus his epistemology a form of hermeneutics. Today hermeneutics is accepted as an important philosophy of science. Kuhn seems not, however, to have made the implication for scientific methods, as we shall here suggest: that scientists should not just learn about current research and current methodology but should also be taught the history and philosophy of science. As Ross (1990, 814-5) suggested, many scientists and philosophers

would welcome a rapprochement between science and philosophy. This would, in effect, involve a breaking down of Kuhn's distinction between normal and revolutionary science, so that even during ‘normal’ periods, scientists maintained more of an interest in fundamental concepts and methodology.

This view is supported by the following quote by Albert Einstein (1949, 683-4):

The reciprocal relationship of epistemology and science is of noteworthy kind. They are dependent upon each other. Epistemology without contact with science becomes an empty scheme. Science without epistemology is — insofar as it is thinkable at all — primitive and muddled.

This then, is the core of historicist epistemology: That all concepts, observations and deductions must be understood in their social, historical and paradigmatic contexts. For example, concepts such as star and planet [44], or blackbird [45], are imbedded within paradigms, and cannot be understood disconnected from the theories in which they are used (this principle is a form of “semantic holism”).

Historicism implies the ideal that science develops as a dialog between different views. However, at this point Kuhn’s view has been criticized by both Fuller (2000) and Agassi (2008, 306-34) for conservatism and for not defending criticism. Agassi (307) wrote: “Controversy is a vital and regular factor in the scientific tradition. Kuhn did not do it justice”. Both Fuller and Agassi preferred Karl Popper’s philosophy “critical rationalism” because of its emphasis on scientific criticism. However, Popper’s view was opposed to historicism [46].

In the wake of Kuhn (1962) a controversy between realists [47] and antirealists became important. This is further discussed in Section 4.1.1.

The main difference between rationalism and empiricism on the one side, and historicism and pragmaticism on the other side, is the acknowledgement of the socio-historical dimension of science in the latter positions. Rationalism, empiricism, and positivism understand the scientists as individuals facing parts of the world directly (for example, in laboratories). Historicism and pragmaticism understands the scientists as informed by scientific traditions and subject literatures. Not just direct sense experience, but also the reliance of experts and the social division of cognitive labor become important [48]. For these positions, the library is an important addition to the laboratory as a metaphor [49]. Scientific knowledge grows from interaction with former knowledge and with the world, but not from an uneducated, direct interaction with the world.

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3.4 Pragmatism [50] (with Marxism [51], critical theory [52] and feminist epistemology [53])

Pragmatism shares the view of historicism described above. Classical pragmatism mostly preferred to speak about “inquiry” rather than scientific research, and to consider common sense a rudimentary form of science (cf., Rydenfelt 2014). Pragmatism’s main distinguishing characteristics in relation to historicism is the pragmatic maxim (Peirce 1878):

Consider what effects, that might conceivably have practical bearings, we conceive the object of our conception to have. Then, our conception of these effects is the whole of our conception of the object.

Pragmatism emphaticizes the functions of both the research object and the implications of inquiry for practice [54]. (Although it should not be confused with the everyday language meaning as an attitude with overly tight focus on practicality.) It is the category of epistemological theories, that consider goals, teleology [55], purposes, consequences, interests, and values [56] — in one word: politics [57] — as a central point of view. It has been suggested that science is a moral project [58].

There are, however, obvious, and profound problems associated with the relations between science/inquiry and politics. Politicians should not decide what is true, and it is a really bad thing when people do not search for truth but ignore existing arguments and evidence, and only believe and argue what they want to be true. A prerequisite for science has always been the opposite of using political power to manipulate knowledge, it has always been a critical role of science speaking truth to power. There can be an unholy alliance of ignorance and manipulation that is mutually supportive. In this sense “politicized science” and “political epistemology” are things that is opposed to all academic ideals and which are to be seriously fought. These terms also have quite different meanings, however, some of which falls under our category “pragmatism” [59]. “Political epistemology” is also understood (e.g., by Omodeo 2019) in a way that falls under our category pragmatism.

Pragmatism is based on the view that all our actions, including our choice of scientific methods, have political consequences. By implication claimed neutral epistemologies are just neutral by claim, not by consequences. It can be said that claimed neutral epistemologies just disguise their subjectivity as objectivity (see further Hjørland 2020). Pragmatism cannot, however, be understood by the intentions of the researchers. For example, although socialist-minded researchers may claim to serve the interests of the working people, liberalists-minded researchers may deny that this is the case. It may always be questioned whether the interests claimed or intended also are the interests that in the end are supported [60]. It is not just the motivations and intentions of research that matters, it is the outcome, and implications of research may be hard or impossible to predict (cf., Koertge 2000).

The problem of having explicit goals in science is a problem that has split pragmatists already from Peirce and James. Peirce disagreed with William James on the interpretation of pragmatism because he felt that James made it too vulgar and short sighted, and he renamed his own position pragmaticism to distinguish it from James’ version. Ever since there has been a split between “realist” or “objective” and “antirealist” or “subjective” pragmatists [61]. Peirce (and some other pragmatists along with some Marxists and feminists) obtain, however, that pragmatism and realism are not opposed to each other, but are each other’s prerequisites (see further Westphal 2017).

The pragmatic theory of meaning is revealing for how consequences may guide pragmatic thinking. Peirce (1905, 173-4) wrote:

The rational meaning of every proposition lies in the future. How so? The meaning of a proposition is itself a proposition. Indeed, it is no other than the very proposition of which it is the meaning: it is a translation of it. But of the myriads of forms into which a proposition may be translated, what is that one which is to be called its very meaning? It is, according to the pragmaticist, that form in which the proposition becomes applicable to human conduct, not in these or those special circumstances, nor when one entertains this or that special design, but that form which is most directly applicable to self-control under every situation, and to every purpose. This is why he locates the meaning in future time; for future conduct is the only conduct that is subject to self-control.

This quote gives an idea of some aspects of the pragmatic methodology: to consider conceptions and theories identical if they lead to the same consequences (regardless of their differences in other ways), and always have the consequences in mind. Pragmatism assumes empirical studies as a basis of enquiry, but differs from classical empiricism in several philosophical assumptions:

  • Pragmatism has a much richer account of the concept of experience compared to classical empiricists (which is related to phenomenology’s “life world” and to feminist epistemology’s claim of the relevance of persons broader experiences).
  • Pragmatism (at the least in Dewey’s version) acknowledges the role of not just science, but also art and everyday life as valid forms of knowledge [62].
  • Pragmatism sees the inquirer as influenced by socio-cultural factors and the process of inquiry as a chain of “unlimited semiosis” [63].
  • Pragmatism assumes that realism cannot be proved by science but that it is a necessary assumption in inquiry (cf. Rydenfelt 2014). This may be called “pragmatic realism” or “realistic pragmatism” [64].
  • Pragmatism involves the willingness to embrace fallibilism (that we should be open for the possibility that even our best based theories may have to be revised as science proceeds).
  • Pragmatism involves the rejection of skepticism (rejection of the view that one can and should try to doubt all of one’s beliefs at once). According to Putnam (1994, 152; italics in original) “it is perhaps the unique insight of American pragmatism that someone can be both fallibilist and antiskeptical”; see further in Hookway (2008).
  • Pragmatists tend to reject sharp dichotomies such as those between fact and value, thought and experience, mind and body, analytic and synthetic, basic and applied science [65] etc.
  • Pragmatists favor ‘the primacy of practice’. This point is clarified by the way Sarvimäki (1988, 58-9) emphasizes that living and acting in the world according to pragmatism constitutes the a priori of human knowledge [66] [67] [68]).
  • Pragmatists tends to reject “the correspondence theory of truth” and to favor “the coherence theory of truth” [69]. Hoyningen-Huene (2013, 170; italics in original) found: “The best way to realize this relation of coherence for a given knowledge claim is to embed it into a system of knowledge claims”.
  • In addition to induction and deduction, pragmatism favors abduction, i.e., inference to the best explanation (Peirce’s 1903 lecture has the title “Pragmatism as the Logic of Abduction”).

We have mentioned some important philosophical principles, and we have on the one hand claimed that political goals are important, and on the other hand that politicized science is a really bad thing that must be fought. The normative principles may be formulated as follows:

In all domains of knowledge there will always be conflicting views of which claims are correct and which are wrong. Such claims are connected to broader theories, epistemologies, and ideologies, each of which may have hidden assumptions and consequences. The inquirer should — as far as possible — be aware of such different views and on the different goals and values they support and choose his epistemology on an informed basis. This requires a broader knowledge compared to narrow subject knowledge, which includes historical and philosophical knowledge (Slife and Williams 1995 provides such knowledge for the behavioral sciences and is a model of the kind of knowledge, we here are speaking of). More specifically, pragmatic epistemology implies that no research claim should be considered validated without the inclusion of epistemological arguments. It must be emphasized, however, that the philosophical and historical knowledge of which we are speaking itself is a fallible and developing body of knowledge.

In Section 4.3.2 about social constructivism, we shall see how the pragmatic view and its focus on social interests has been taken up by science studies.

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3.5 Conclusion on method

Debates on scientific method are parts of a broader philosophical development, which have been described as historicist and as a strong challenge “to the most favored doctrines of the principal currents of Anglo-American philosophy down to our own day” (Margolis 2009, x) [70]. In line with this view Schuster (1995a, 45) considered the scientific method a myth, because facts and tests depend on theory and prior belief:

If there is always such a cultural loading of the facts available to humans, then this would necessarily entail problems for the standard story of method: Remember, if anything gets into science, into laws and theories, such as subjective belief, cultural baggage, human political, social concern, then we do not have what scientific knowledge is supposed to be, coagulated fact, which has been tested and confirmed.
That is, the standard story of method absolutely demands and requires that pure, 'nuggets' of fact are available from nature with no admixture of human subjectivity, culture, prior belief etc. But, if human facts are shaped or conditioned by human beliefs and aims, then science becomes a much more complex institutional activity—political, social, historical aspects need to be studied to understand how science makes facts, sustain facts, and sometimes changes facts.

Schuster’s quote seems a fine conclusion of our section about the scientific method as it moves the problem from rationalism and empiricism towards historicism and pragmatism. Inductive, deductive, and abductive methodologies should be seen as iterative processes taking place in socio-historical and political contexts and within “paradigms”.

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4. The study of science

There is a need for an overall term for all kinds of philosophical, theoretical, historical, and empirical studies of science. Daston (2015, 242) suggested:

The phrase ‘science and technology studies’ bears witness to these criss-crossing ties to other disciplines, serving as an abbreviation for the conglomerate ‘history, philosophy, sociology, and anthropology of science, medicine, and technology’, which, however cumbersome, accurately reflects the ecumenical perspective of many historians of science.

However, very often, the term “science studies” is understood as sociological and anthropological studies of science (and sometimes with a certain theoretical commitment towards constructivism) and excluding the philosophy of science. Just as we need “science” as broad, inclusive term for all kinds of scientific and scholarly inquiries, we need a broad meaning of “science studies”. Sometimes “metascience” or “science of science” are used (e.g., Radnitzky 1970; Bourdieu 2004; Goldsmith 1967), but these labels are also used both narrowly about empirical research or broadly about empirical, historical, theoretical, and philosophical studies of science. “Science studies” seems to be the mostly used term today, and, in opposition to “science of science”, it also avoids the connotation associated with “science” as being limited to natural scientific studies of science [71]. Here we prefer the term science studies as the broad overall concept, although in Section 4.3 the term is used in its narrow meaning.

Three main groups of science studies are: (1) Philosophy of science, (2) History of science, (3) Sociology of science. To this comes a range of other disciplines, including psychology and cognitive studies of science, scientometrics, information science, knowledge organization, genre and terminology studies (and much more, not to be further introduced, including economics of science, pedagogy of science, science management etc.).

All these disciplines are interdependent, although this is not acknowledged in all traditions. For example, it is the tradition following Kuhn (1962) that emphasizes the connection between the philosophy and the history of science, whereas the analytic philosophical tradition remains rather ahistorical. All studies of science require domain knowledge about the specific field studied but are different from domain knowledge by providing specific perspectives about the domains [72].

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4.1 The philosophy of science

The philosophy of science addresses problems such as scientific methodology[73] (as considered in Section 3), the objectivity and robustness of scientific claims, the unity [74] versus disunity [75] of science, the demarcation problem (how we distinguish science from non-science and from pseudoscience), scientific theories and laws, models, what are natural kinds (if any exist) [76], and much more (see, e.g., Newton-Smith 2000 and the voluminous Handbook of the Philosophy of Science (Gabbay, Thagard and Woods 2006-) [77]. Only a few issues in the philosophy of science can be presented in this article [78].


4.1.1 Metaphysical issues

Two important controversies became important in the wake of Kuhn (1962): (1) the discussion between realists (or materialists) on the one side and antirealists (or idealists [79]) on the other side and (2) the discussion between relativists on the one side and absolutists on the other.

Realists claim that scientific objects, e.g., atoms, animals, cells and the Milky Way, exist independent of the conceptual frameworks in which they are expressed; antirealists claim the opposite. There are many views about realism and Kuhn was unclear on this issue and has been used to argue for what is often understood as antirealist positions like forms of social constructivism and postmodernism, although he (Kuhn 2000, 110) distanced himself from such interpretations and rejected “the Strong Program” in the sociology of science as “deconstruction gone mad” [80] (see further on the Strong Program in Sections 4.3.2.2 and 4.3.3).

Haack (2009, 336) described the situation as follows:

[T]he last thirty years or so have seen a major shift: from the Old Deferentialist view, which took science to deserve a kind of epistemic authority in virtue of its peculiarly objective method of inquiry; to a New Cynicism, which sees science as a value-permeated social institution, stresses the importance of politics, prejudice and propaganda, rather than the weight of the evidence, in determining what theories are accepted, and sometimes goes so far as to suggest that reality is constructed by us, and ‘truth’ a word not to be used without the precaution of scare quotes.

Under the name New Cynicism, Haack (2004, 35) included “radical feminists, multiculturalists, sociologists and rhetoricians of science, and […] a good many philosophers as well”. She finds that the Old Deferentialism focuses too exclusively on the logical, the New Cynicism too exclusively on the sociological factors, that an adequate philosophy of science should combine and that truth lies in between these positions. The natural sciences have been the most successful of human cognitive endeavors, but they are fallible and imperfect—not entirely immune to partiality and politics, fad, and fashion. Haack works from a position inspired by Peirce’s pragmatism, which may be termed “pragmatic realism”[81].

It seems that both realists and antirealists have important arguments and views to defend, which need to be considered by any well-developed position. In Section 4.3.3 we shall see how the Strong Program has provided new arguments in support of the view that human knowledge at the same time reflects a mind-independent reality and human interests.

Relativism is, according to McAllister (2000, 405), the claim that the sentence “entity E has property P” should rather be formulated “entity E has property P relative to S”, in which S can be cultures, world views, conceptual schemes, practices, disciplines, paradigms, styles, standpoints or goals. Relativism about P therefore implies that P is a relation rather than a one-place predicate. Many kinds of relativism are entirely unobjectionable, for example, the property “utility”. However, relativism about truth-value, about rationality or the evidential weight of empirical findings are debated and have been a central issue in the so-called “science wars” (cf. Section 4.3.2.1).

The founder of the Strong Program in the sociology of science, David Bloor (2015, 595), acknowledged that he considered himself a relativist:

[I]f ‘relativism’ is simply the denial of ‘absolutism’, and the rejection of absolutism is a necessary and sufficient condition for relativism, then the Strong Program is relativist, and rightly so. […]
Critics thus use an eclectic definition [of relativism], but in so doing they conflate questions that should be kept separate. They run together different intellectual traditions and fail to draw obvious distinctions. The dichotomy between absolutism and relativism is not the same as the dichotomy between idealism and materialism or between rationalism and irrationalism.

Further information about metaphysical research may be found in encyclopedias such as Kim and Sosa (1995) and in handbooks such as Loux and Zimmerman (2005). It should also be said that metaphysics is closely related to the philosophical field of ontology (see Poli and Seibt 2010), which has gained importance in information and computer science for the construction of ontologies, as systems for organizing knowledge, and thereby has an applied dimension (see Poli, Healy and Kameas 2010).

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4.1.2 The Demarcation problem

We already considered the demarcation problem from Section 1, because one cannot discuss the term science without considering what it includes and what it excludes (e.g., whether the humanities qualify as sciences). In this place, a few issues will be added. First, different concepts must be distinguished. Barseghyan, Overgaard and Rupik (2018, Chapter 6, electronic source no page, italics in original) wrote:

Historically, many philosophers have sought to demarcate science from non-science. However, often, their specific focus has been on the demarcation between science and pseudoscience. Now, what is pseudoscience and how is it different from non-science in general? Pseudoscience is a very specific subspecies of non-science which masks itself as science.

There are many terms related to non-science and pseudoscience [82], including, but not limited to, fringe science [83], junk science, occult science [84], parascience [85], pathological science [86], pre-paradigmatic science [87], protoscience [88] and voodoo science. Although some of these terms are used with relatively stable meanings, there seems not to be a general agreement about the terminology of different kinds of non-science. It is, however, important to distinguish non-science from pseudoscience: While one may, for example, consider the humanities part of non-science, it would be wrong to consider the humanities as pseudoscience.

One may ask: what is the discussion of the demarcation problem important for? Mahner (2007) and Hansson (2017) noted that demarcations were particularly important in practical applications such as healthcare, expert testimony, environmental policies, science education and journalism. Laudan (1983) however, found no benefits by philosophers’ attempt to define a set of criteria that distinguishes science from nonscience, and his article intended to close the debate about this problem. He wrote (119; italics in original):

No one can look at the history of debates between scientists and 'pseudo-scientists' without realizing that demarcation criteria are typically used as machines de guerre in a polemical battle between rival camps. Indeed, many of those most closely associated with the demarcation issue have evidently had hidden (and sometimes not so hidden) agendas of various sorts.

Laudan warns against the attempt to make demarcation criteria, which he considered a philosophical pseudo-problem (but he maintained the importance of the question: “What makes a belief well founded (or heuristically fertile)?” Which he finds should not be confused with the question: “What makes a belief scientific?”).

The present author considers that the debate about the demarcation problem may illuminate the question about the meaning of science: Any attempt to describe and classify something presupposes a clarification of the concept, i.e., what it includes and excludes.

Concerning the history of attempts to solve the demarcation problem Laudan (1983) distinguished “the old demarcationist tradition” (from Aristotle to late Nineteenth century) and “the new demarcationist tradition” (from logical positivists and Popper and forward).

Laudan (1983, 112; italics in original) wrote on the old tradition:

In his highly influential Posterior Analytics, Aristotle described at length what was involved in having scientific knowledge of something. To be scientific, he said, one must deal with causes, one must use logical demonstrations, and one must identify the universals which 'inhere' in the particulars of sense. But above all, to have science one must have apodictic certainty. It is this last feature which, for Aristotle, most clearly distinguished the scientific way of knowing. What separates the sciences from other kinds of beliefs is the infallibility of their foundations and, thanks to that infallibility, the incorrigibility of their constituent theories. The first principles of nature are directly intuited from sense; everything else worthy of the name of science follows demonstrably from these first principles. What characterizes the whole enterprise is a degree of certainty which distinguishes it most crucially from mere opinion.

The new demarcationist tradition, dominated by the logical positivists in the 1920s and 1930s was not based in epistemology and methodology, but in a theory of meaning. They suggested that a statement was scientific in the case it had a determinate meaning, where meaningful statements were those which could be exhaustively verified. For the positivists verifiability, meaningfulness, and scientific character all coincide. As a would-be demarcation between the scientific and the non-scientific, Laudan (1983, 120) found that it was a disaster: “Not only are many statements in the sciences not open to exhaustive verification (e.g., all universal laws), but the vast majority of non-scientific and pseudo-scientific systems of belief have verifiable constituents”.

We will end this section by considering the relation between the demarcation problem and conceptions of science. It seems obvious that non-science is the opposite of science and therefore that any conception of science implies what is respectively “science” and “non-science”. For example, if science is understood from the empiricist-inductivist point of view, then non-science is by implication what does not live up to empiricist norms. If science is understood as in Popper’s philosophy as the attempt to falsify theories, then by implication non-science are the theories which do not have clear criteria for how they can be falsified. If science is understood from a Kuhnian perspective, then the demarcation criterion is sustained support of a puzzle-solving tradition. This insight indicates that the demarcation problem is not an independent problem, but a by-product from insights achieved by the philosophy of science [89].

Most attempts to provide demarcation criteria tend to consider different fields as monolithic. A more constructive approach could probably be to criticize problematic tendencies in different fields and indicate which kinds of scientific practices and behaviors should be discredited. For example, tendencies to disregard or distort arguments from opponents can be considered a sign of bad scholarship. Much of what today carries the attractive label “science” seems to be dominated by a flood of low-quality papers. The suggestions by Mahner (2013) to consider “a cluster demarcation” based on a comprehensive checklist of science/pseudo-science indicators and providing a profile of any given field based on a thorough analysis rather than a clear-cut assessment seems closer to this idea than most other contributions [90]. We seem to come back to Laudan’s suggestion to change the question to: “What makes a belief well founded?”

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4.1.3 The classification of the sciences

The classification of the sciences (not to be confused with scientific taxonomy/classification in the sciences [91]) seems today almost to have disappeared as a philosophical field of research. As library scientist Francis Miksa (1998, 34) wrote:

During the nineteenth century, the classification of the sciences became an activity of enormous propositions among a wide number of participants. I sometimes speak of it as a time when anyone who was anybody in the realm of scholarship wrote a treatise on the subject. [92]

And (48): “The movement to classify knowledge [93] and the sciences ended just after the beginning of the twentieth century, a fact treated by R.G.A. Dolby [1979, 167 and 187-88]”.

This field is mentioned here, because it is of great interest to the field of information science and knowledge organization, and there are few scattered, but important philosophical contributions, including Sandoz (2018) and Midtgarden (2020), although most research today comes from bibliometric → science mapping (see Petrovich 2020).

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4.2 The history of science

There is an overwhelming number of descriptions and interpretations of the history of science, both general (e.g., The Cambridge History of Science, 1-8 [94]) and about single periods (e.g., Companion to the History of Modern Science) [95] or the single fields of knowledge (such as medicine [96], physics [97], psychology [98] and the humanities [99]). Such histories are written from many different perspectives, for different audiences and may be highly qualified or of a problematic standard (not seldomly they reproduce myths based on problematic readings of the primary literature) [100]. There are also an overwhelming number of scientific biographies and works on single concepts (e.g., objectivity, experiment, theory, and progress) and much more. Although all works are necessarily written from some point of view (as there can be no “view from nowhere”) only some works are explicit about their views (e.g., feminist, Marxist or constructivist views), but an informed reader may be able to characterize the view, which dominate a certain work. Theory and principles about doing research in the history of science are labeled historiography of science (an example is Agassi 2008).

About different schools in the history of science, Agassi (2008, 31) wrote:

Two philosophical schools of thought support the thesis that science is always right, and they gave rise to two schools of historians of science. The majority (Baconian) school is the inductivist or a posteriorist: science is always right as its ideas are firmly based upon experience. The minority (Duhemian) school is the conventionalist; scientific ideas are mathematical conventions. Although my sympathy, if forced to choose, is unquestionably with the minority against the majority, I belong to neither schools [sic!]. Rather, I find much more congenial the view of Karl Popper of science not as a body of solid knowledge but as a succession of ideas and of the attempts to criticize them, with no end in sight.

The present article cannot go into a deep analysis of historiographic philosophies, but the working hypothesis is that empiricist, rationalist, historicist, and pragmatic philosophy will turn out to represent the deepest level also in the historiography of science.

Two further issues to be introduced are (1) a way of history writing called “whiggish” and (2) problems in periodization in the history of science.

Whiggish historiography has been characterized as the ‘great man’ view of science or as “a particular pathology of history writing” (Schuster 1995b, Chapter 3, 14) and (the same source) as a simplified writing of history consisting of “good guys” or “bad guys”, where the good guys are those who made steps toward present day views in science. The writing of biographies as “good guys” has been termed whiggish hagiography and the opposite whiggish demonology. Roos (2018, 195) expressed: “Accounts of Eureka moments are a favourite in these works; the ‘compression of decades of work into a single inspirational moment is entirely characteristic of these parables”. Schuster (1995b, 17) concluded his short chapter “The Problem of ‘Whig History’ in the History of Science”:

We are going to see that Whiggish history of science depends upon and reinforces the three key myths about science — method, autonomy and progress. Hence we shall see that all these beliefs stand or fall together. If they stand, we remain at the level of cultural myth and mystification in our understanding of Western Science; if they fall, the possibility of a demystified historical understanding of science emerges, and that is where we are headed over the next twenty-three Chapters.

We cannot here go deeper into the debates of whiggish historiography, but the reader should consider that the problems are more complex than the description given here (see, for example, Alvargonzález 2013).

Shaw (2020) introduces the issues related to → periodization in this encyclopedia. Gabovich and Kuznetsov (2019) presented some of the plenty criteria for temporal classification of science, which have been proposed in the literature, and they defend a theory-grounded periodization of science. We shall not consider this view further, but emphasize an important issue:

  • Internalism in the historiography of science views the development of science distinct from social influences, but determined by the knowledge generated within a science itself (or more or less interdisciplinarily influenced)
  • Externalism in the historiography of science is the view that the history of science is due to its social context.

These two views on the development of science are still the subject of analysis. Lakatos (1978, 118—122 and 190) attempted to explain the distinction between internal-rational and external-empirical history of science, which has been criticized but recently supported (see, e.g., Dimitrakos 2020). Omodeo (2019, 2-3) found that this distinction was maintained by the Cold War [101].

The writing of the history of science (in general or a specific science) can thus be guided by opposite assumptions. Abrahamsen (2003, 149-51) described how two Danish histories of music described the same field (the history of music) in vastly different ways regarding, for example, periodization. In one of them, based on a “style paradigm” (but relatively uninterested in theoretical explanations), the history of music is seen as fundamentally different from general history because of music’s aesthetic character, and it expresses the implicit view that the musical work is relatively autonomous. Consequently, this work focuses more on the composers and performer’s role in the development of music. In the other work (based on a materialist philosophy, and more explicit about its epistemological commitment) the culture of music is viewed as a part of a historical process, where the music is included in an interaction with political, social, economic, and ideological elements, and the description of the music’s function in this interaction is this book’s main concern.

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4.3 The sociology of science and “science studies”

Collins and Evans (2002) outlined three waves of science studies:

  • The first wave (about 1950-1970) was characterized by the aim (p. 239; italics in original) “at understanding, explaining and effectively reinforcing the success of the sciences, rather than questioning their basis […]. This wave of ‘positivism’ began to run into shallow academic waters in the late 1960s with Thomas Kuhn’s book and all that followed. By the end of the 1970s, as an academic movement, it had crashed on to the shore”.
  • The second wave (from about 1970 until today) has shown that it is (p. 239) “necessary to draw on ‘extra-scientific factors’ to bring about the closure of scientific and technical debates — scientific method, experiments, observations, and theories are not enough”. In this phase “sociologists have become unable to distinguish between experts and non-experts”.
  • The third wave Collins and Evans (2002) labeled “studies of expertise and experience” (SEE) and is described as something that may already exists in embryonic form (in 2002) and which their article is a further argument for. The authors suggest (p. 238) that it “should accept the Second Wave’s solution to the Problem of Legitimacy [that the basis of technical decision-making can and should be widened beyond the core of certified experts], but still draw a boundary around the body of ‘technically-qualified-by experience’ contributors to technical decision-making”.

Collins and Evans (2002) found that sociologists in the second wave have dissolved some dichotomies and classes and left a need to build new ones based on a “normative theory of expertise”. In the present paper we consider three schools of science studies. The first (4.3.1 Merton) represent the first wave, while the other two (4.3.2.2 The Strong Program and 4.3.2.3 Bruno Latour and “actor-network theory”) are classified as constructivist and mostly representing the second wave. We have here no specific coverage of anthropology, but Bruno Latour is also considered an anthropologist of science. An early, important contribution from anthropology is Elkana (1981).

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4.3.1 Merton

Robert King Merton (1910—2003) has been called the “founder of the sociology of science” and ”undoubtedly the most important sociologist of science” (Cole 2004, 843). In his dissertation (Merton 1938), he asks why it is that science emerged so strongly in the third quarter of the 17th century in England and why this particular institution does well in one society at one point in time. His answer was that science flourishes in societies in which scientific activity is highly regarded by the society at large. Among the specific concepts and theories developed by Merton the following can be mentioned:

  • “CUDOS”: an acronym with four (in some versions five) normative principles, comprise the ethos of scholarship, introduced by Merton (1942) and later modified:
    • Communalism (originally: ”communism”): all scientists should have common ownership of scientific goods (intellectual property), to promote collective collaboration; secrecy is the opposite of this norm.
    • Universalism: Scientific validity is independent of the sociopolitical status/personal attributes of its participants.
    • Disinterestedness: scientific institutions act for the benefit of a common scientific enterprise, rather than for the personal gain of individuals within them.
    • Originality: the commitment to the pursuit of new knowledge. “Whereas objectivity is a value that works to safeguard the truth of science, originality is a value that works to safeguard it from stagnation” (Sztompka 1986, 52).
    • (Organized) Skepticism: scientific claims should be exposed to critical scrutiny both in methodology and institutional codes of conduct. Science must systematically examine claims, be anti-authoritarian and skeptical.

    Merton considered these norms as ideals, not as descriptions of the actual behavior of researchers.

  • “Foci of attention”: What determines researchers’ choice of topics/research problems? Merton (1938) demonstrated that scientists were strongly influenced by the practical concerns of the day, such as navigation (this can be interpreted as a support of the pragmatic epistemology presented in Section 3.4).
  • “Matthew effect” or “the Matthew effect of accumulated advantage” is an expression introduced by Merton (1968) derived from the Bible (Matthew 25:29): “For to everyone who has will more be given, and he will have abundance; but from him who has not, even what he has will be taken away”. It is sometimes summarized as “the rich get richer and the poor get poorer”. In science studies it relates to the claim that scientists who have had an advantage tends to be overrated. In bibliometrics is has been used to claim that those who are known and have many citations will get more citations than they deserve.
  • “Multiple discovery” are discoveries made independently by more than one researcher. Merton (1963, 307; 1996) found that more researchers independent of each other often make such discoveries. By implication, scientific development does not depend on a few geniuses, but the geniuses just accelerate the process.
  • “Obliteration by incorporation”: Certain ideas become so universally accepted and commonly used that their contributors are no longer cited. Eventually, its source and creator are forgotten (“obliterated”) as the concept enters common knowledge (is “incorporated”). Obliteration occurs when “the sources of an idea, finding or concept, become obliterated by incorporation in canonical knowledge, so that only a few are still aware of their parentage” (Merton 1968, 27-8).
  • “Serendipity” (unplanned, fortunate discovery) (Merton and Barber 2004). This concept has derived much research in information science. However, as pointed out by Carr (2015), serendipity in the stacks can usefully be framed as a problem: “From a process-based standpoint, serendipity is problematic because it is an indicator of a potential misalignment between user intention and process outcome”.
  • “Uncitedness”. Merton (1977, 54-5): “For if one's work is not being noticed and used by others in the system of science, doubts of its value will arise”.

Merton was, prior to the time of his death, the most famous living sociologist of science. Merton’s sociology of science has, however, been criticized on different levels. Cole (2004) contains a rather serious criticism of the research done by Merton (and even of Merton as a person). He claims, for example, that the Matthew effect simply is wrong when tested empirically. The main issue in later generations of sociologists and researchers associated with social constructivism is however, that Merton’s view is considered “weak” because it left the cognitive content of science out of the sociological explanation [102]. Merton explained the social conditions for scientific flourishing, but he did not ask which social conditions made science construe the contents of scientific theories.

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4.3.2 Social constructivism

4.3.2.1 Introduction

By contrast to Merton’s sociology of science, most newer researchers in the sociology of science have been associated with the label “social constructivists” (but not all, Stephen Cole, for example, is an exception), and there are wide divergences in views within constructivism. The different meanings of the term have been clarified by Hacking (1999). Golinski (1998, ix), who characterized his own attitude as sympathetic but not uncritical towards constructivism, defined the term in this way:

By ‘constructivist’ outlook, I mean that which regards scientific knowledge primarily as a human product, made with locally situated cultural and material resources, rather than as simply the revelation of a pre-given order of nature”.
In this broad understanding, constructivism is very much in line with the pragmatic view described in Section 3.4 (where pragmatism was characterized by seeing the inquirer as influenced by socio-cultural factors). However, as already said, constructivism consists of many different views, of which just two are briefly outlined below.

Constructivist theories have been considered as, on the one hand, liberating, and on the other hand as harmful ideas. They are considered liberating ideas because they imply that we do not have to accept scientific knowledge claims, but can engage in alternatives, which we find more satisfactory. As formulated by Hacking (1999, 6-7):

Social construction work is critical of the status quo. Social constructionists about X tend to hold that:
(1) X need not have existed, or need not be at all as it is. X, or X as it is at present, is not determined by the nature of things; it is not inevitable.
Very often they go further, and urge that:
(2) X is quite bad as it is.
(3) We would be much better off if X were done away with, or at least radically transformed. […]
X was brought into existence or shaped by social events, forces, history, all of which could have been different.

From the other view, constructivism is considered harmful by tending to undermine science. There have even been “science wars” between scientists and philosophers on one side and constructivist sociologists on the other side. These “wars” have, according to Hacking (1999, x) “temporarily destroyed the possibility of friendly discussion and scholarly collaboration”. Hacking (67) also writes that “many science-haters and know-nothings latch on to constructionism as vindicating their impotent hostility to the sciences” but he also suggests (68) that this is not the case for leading constructivists such as Pickering and Latour.

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4.3.2.2 The Strong Program

The so-called Strong Program (or “the Edinburgh School”) in the sociology of science was formulated by an interdisciplinary group of researchers in Edinburg in the 1970s (for introductions see Barnes, Bloor and Henry 1996 and Bloor 2015).

The principles formulated by Bloor (1976) elaborated four principles guiding the strong program:

  • The first principle is that sociology, as science, has to offer causal explanations.
  • The second principle is that sociology needs to investigate social beliefs without imposing the standards of the investigator upon the subject of investigation.
  • The third principle, reflexivity, suggests that the theory of sociology should not be immune to its own argument; it must be possible to conduct a sociology of sociology.
  • The final principle, symmetry, holds that both true and false, and rational and irrational ideas, in as far as they are collectively held, should all equally be the object of sociological curiosity, and should all be explained by reference to the same kinds of cause.

However, in practice, the studies made by this school have been more philosophical and historical than they have been empirical-sociological (cf. Hacking 1999, 37). This seems important because the suggested ideals for the empirical studies seem to reflect the ideals of logical positivism (Collin 2011, 37ff.), whereas the philosophical principles derived from or applied to the case-studies seem to provide a clear alternative to positivism and to be related to pragmatism.

The strong program defends a view of knowledge, “interest theory” (cf. Barnes 1977), in which all knowledge is considered social and interests influence the scientific process. This program, as constructivism overall, opposes the view that sociological analysis can only explain knowledge that is in error, whereas true knowledge remains insulated from social forces.

Constructivism has provided much controversy and social explanations of scientific claims have been considered incompatible with realism: that scientific claims are true claims about the world. This question about whether the strong program represent a realist position or not, is much debated. Barnes, Bloor and Henry (1996, 81-88) explained their realist position carefully, but many others deny this and find constructivism to be “idealist” (e.g., Downes 1998) [103]. Bloor (2015, 592-3) discussed and refuted what he called “The False Charge of Idealism”.

The examples from science given by Bloor (1982), and the application of the network model, provide convincing arguments for the possibility for systems of knowledge to reflect society and be addressed to the natural world at the same time. They demonstrated how social interests were active in the development of scientific knowledge. Bloor provides little guidance, however, in determining how sociological research may help us determine contemporary scientific controversies (including classification problems in science such as the Periodical system in chemistry and physics and biological classifications, which are still subject to scientific debates) [104].

Slezak (1994) provided an extremely critical review essay of Bloor (1991) and wrote (338):

Certainly, the contingent, causal connection claimed to hold between science and society, content and context, entails that we should be able to predict the substance of scientific theories given the details of the social, cultural milieu. Recall that the much-touted case studies of the Strong Programme are taken to have established precisely this kind of connection. However, Popper offers a formal, logical argument to the effect that no scientific method can possibly yield its own future results, and hence predicting the future course of human history is also impossible to the extent that this is influenced by the contents of scientific theories.

This criticism seems not to have been answered by Bloor, but Suchting (1997) provided a fruitful analysis with the overall conclusion “that each account [Slezak’s and Bloor’s] alludes to different and crucial aspects of the nature of knowledge without, severally or jointly, being able to theorise them adequately”, and then suggested his own twelve epistemological theses for a more adequate theory of knowledge.

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4.3.2.3 Bruno Latour and “actor-network theory”

Latour is a very influential figure in the sociology and anthropology of science. His research includes detailed empirical studies of scientists work in their laboratories, ontological theorizing, and political speculation. His position is complex and difficult to summarize.

As an example of Latour’s empirical research is the book Laboratory Life (Latour and Woolgar 1979; 1986 [105]), which describes his empirical observations of the endocrinological researchers in the Salk laboratories in San Diego, who discovered the thyrotropin releasing hormone (TRH) and (together with others) were awarded the Nobel Prize in 1977. There is extraordinarily little TRH in the world. To study it, five hundred tons of big brains had to be shipped to the laboratory on ice to distill just a microgram of TRH. Hacking (1999, 175) wrote:

And what was this TRH? It was a substance that passed certain assay tests. But there was no agreement on what the assays should be, and different labs had different assays. The winning labs ‘determined’ the assays and so determined the practical criteria of identity for TRH. Second, when a certain peptide had been synthesized, and declared to be TRH, that was the end of the matter. The drug company that had sponsored much of the research patented and started selling synthetic TRH.
The question as to whether this really is TRH simply dropped out, with the skeptics turning their minds to other things. Synthetic TRH became a laboratory tool in its own right.

According to traditional scientific norms, new research demanding hundreds of tons of brains should have continued until consensus has established what TRH is. But it did not. As Hacking (176) wrote: “Who will collect another 500 tons of big brain to distill a microgram of whatever it is?” Latour say that TRH is a scientific “fact” that was constructed by the leading lab, and TRH gave rise to a whole new research field, which therefore also is “constructed”. Hacking (1999, 177) offers an alternative interpretation: “A realist need only say that among all the possible facts to be discovered in the endocrinology of the hypothalamus, this particular structure has been singled out and will determine the future possible structures to be discovered, shutting off others from the screen of possibilities”.

Another of Latour’s works, The Pasteurization of France (Latour 1988), analyzed how the French microbiologist, Louis Pasteur, was constructed as a great man, who is said to have revolutionized French agriculture by, among other things, to discover the cause of anthrax and create a vaccine for the disease. Instead of just considering this the work of an individual genius, Latour (in the words of Law 2009, 145), “charts how a network of domesticated farms, technicians, laboratories, veterinarians, statistics, and bacilli was generated. He describes how they were shaped (in some cases created) in this network. And he shows how the result was generative. Farms were turned into laboratories, vaccines made from attenuated bacteria, cattle stopped dying of anthrax, and Pasteur became a great man”.

Latour’s downplaying the role of the individual and emphasis on the collective of which he is a part, is well known from, for example, Marxist theory and from many (non-Whig) historical writings, which emphasizes the background factors of discoveries. Latour’s interpretation is, however, radical. We can recognize his “actor network theory” (ANT) in the example of Pasteur. Law (2009, 141) characterized ANT in the following way:

Actor network theory is a disparate family of material-semiotic tools, sensibilities, and methods of analysis that treat everything in the social and natural worlds as a continuously generated effect of the webs of relations within which they are located. It assumes that nothing has reality or form outside the enactment of those relations. Its studies explore and characterize the webs and the practices that carry them. Like other material-semiotic approaches, the actor network approach thus describes the enactment of materially and discursively heterogeneous relations that produce and reshuffle all kinds of actors including objects, subjects, human beings, machines, animals, “nature”, ideas, organizations, inequalities, scale and sizes, and geographical arrangements. [106]

The radicalism attributed to ANT has particularly focused on ANT’s recognition of subjectivity or agency in other than human beings — and even in other than non-living things. Muiesa (2015, 82) wrote:

ANT is often associated in popular views with an insistence on ‘nonhuman agency’, that is, on sources and agencies of action other than purely human, conscious, and intentional. In fact, ANT stands as a reaction to both the downplaying of human agency in accounts of events favored in the natural and formal sciences (an ellipsis of the action of the experimenter in a microbiology laboratory, for example, in reports of findings) and the downplaying of nonhuman agency in accounts of events favored in the social sciences and the humanities (an ellipsis of the actions of bacteria, the medium, and the laboratory instrument).

The Strong Program and ANT have expressed critical views towards each other. Bloor’s (1999a) article even bears the title “Anti-Latour” (answered by Latour 1999 and re-answered by Bloor 1999b). However, when members of the Strong Program tried to be positive, they (Barnes, Bloor and Henry 1996, 115-6) wrote:

It is important however that we take note of the virtues as well as the limitations of Latour’s work, so let us praise it for its methodological significance. It encourages anyone disposed to consider science from the perspective of political economy to consider every single action [107] without exception in that light, and to refuse to exempt any aspect of science whatsoever from that kind of scrutiny.

Omodeo (2019, 18-19) and Mirowski (2017, 447) found, however, that ANTs depersonalizing of action and subjectivizing of nature [108] undermines the possibility of criticizing or improving science [109]. As we shall see below (Section 4.3.3) Latour seems to admit that his research may have supported unhealthy influences and needs a revision.

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4.3.3 Conclusion on the sociology of science

Merton’s research (and other sociologists outside constructivism) is a respected field which has contributed much to our understanding of science. The role of the many constructivist schools is much more controversial and difficult to evaluate. A key issue is here constructivism’s status as either “realist” or “idealist“ and the related problem of whether constructivism is supporting or damaging science.

Downes (1998) wrote: “These forms of constructivism [explicitly including the Strong Program], claiming that scientists have access to nothing other than representations, are reminiscent of the idealism of Berkeley” [110]. Haack (1996, 263) argues that some social constructionist positions treat natural objects themselves as if they are socially constructed rather than having an independent existence. She does not, however, specify which positions she is referring to, and in relation to the Strong Program, Bloor (2007) seems effectively to have refuted her claim [111]. Barnes, Bloor and Henry (1996) provide a convincing theoretical frame for a sociology of science in which the scientific objects (nature) are included, but still science is understood as also influenced by social interests. In Section 3.4 we discussed the same issue and found that some pragmatists did not see an opposition between pragmatism and realism.

Concerning the issue about whether constructivism undermines scientific argumentation, Kemp (2005, 707-8) wrote:

What Bloor wishes to demonstrate is that constructionists of the Strong Programme variety have been wrongly targeted in this respect. He argues that there is a clear distinction between the perspectives and purposes of scientific actors and social constructionist analysts. Scientific debate is undertaken in order to reinforce or undermine the credibility of scientific claims. Constructionists, on the other hand, take a step back to analyse the field of play from a non-judgemental perspective, examining the construction of scientific credibility without assessing scientists’ claims for credibility. This being the case, constructionist analyses do not challenge or undermine scientific argumentation, leaving it untouched.

This argument seems problematic, however. Should epistemological, historical, and sociological research on science work from a non-judgemental perspective as suggested by Bloor? Feminist epistemology, for example, has critically analyzed “positivist” research and suggested better alternatives (see, for example, Hjørland 2020). Likewise, Kurt Danziger’s research about the social construction of psychological knowledge seems extremely useful for understanding psychology as a science. For example, in Danziger (1997), it is shown how intelligence was constructed as a psychological concept, and how this construction was connected to social interests such as the military and the school system and thereby, at least implicitly, suggesting how psychology could be developed to better serve other interests, such as the needs of teachers and students. It seems rather obvious that the sociology of knowledge should examine which interests are served by given research, and which are relatively harmed or neglected, and thereby, at least sometimes, should try to undermine a given piece of claimed knowledge. However, it should certainly not undermine science as an institution. In this connection Latour (2004) contains a revealing point of view. He wonders if science studies enabled right-wing climate denialism, alternative medicine, and a politics of conspiratorial thinking. Latour says he made a career of asking critical questions of scientists, and now his political foes are using the same kinds of arguments to delegitimize science and breed excessive distrust (p. 227). Therefore, he suggests that science studies change course, likening himself to a general who alters strategies as the battlefield conditions change, suggesting that deconstructivism should become a constructivism, adding to reality rather than merely subtracting from it (p. 232).

Sociology of science after Merton often seems to provide fewer concrete findings, but to focus on historical and philosophical problems: Perhaps for this reason bibliometricians, for example, have often found the new sociology of science unhelpful [112]. The following points is an attempt to list what can be regarded as specific contributions from the new sociology of science:

  • Supporting a pluralist view of knowledge [113] and exposing the contingency of some of our social practices that we have wrongly come to regard as inevitable (e.g., Danziger 1990).
  • Supporting the pragmatic realist view according to which scientific knowledge is at the same time reflecting reality and reflecting human interests (e.g., Barnes, Bloor and Henry 1996)
  • Illuminating the role of gender in science (e.g., Fishman, Mamo and Grzanka 2017)
  • Contributing to describing and understanding the information infrastructures in science (e.g., Slota and Bowker 2017).
  • Describing degrees of consensus in different fields of sciences (e.g., Cole 1992)
  • Contributions to a critical understanding of technology (e.g., Feenberg 2017)
  • Contributions to understanding the relation between science and society and providing new conceptualizations of science (this aspect is further reported in Section 6 below).

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4.4 Other fields studying science


4.4.1 Scientometrics

Scientometrics is a subfield of bibliometrics which studies quantitative issues in science, primarily based on scientific publications; it is an interdisciplinary field with strong basis in information science and the sociology of science, among other fields. A very productive journal in this field is Scientometrics: An International Journal for all Quantitative Aspects of the Science of Science, Communication in Science and Science Policy.

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4.4.2 Cognitive science of science and psychology of science

The role of psychology has been controversial. Some, for example, George Boole, saw logic as part of psychology (cf. the title of his 1854 book An Investigation of The Laws of Thought on Which Are Founded the Mathematical Theories of Logic and Probabilities), whereas, for example, Edmund Husserl devoted the “Prolegomena” in his Logical Investigations (originally published 1900, here cited from the English translation, Husserl 2001b, 40) to a detailed refutation of psychologism, i.e. the thesis that logic is merely a branch of psychology such that logical laws can be reduced to psychological laws.

Willard Van Orman Quine coined the term naturalized epistemology by which he suggested that epistemology should be replaced by psychology. In Epistemology Naturalized (Quine 1969, 78) he wrote:

If all we hope for is a reconstruction that links science to experience in explicit ways short of translation, then it would seem more sensible to settle for psychology. Better to discover how science is in fact developed and learned than to fabricate a fictitious structure to a similar effect.

From the point of view of the sociology of science, Bloor (2007, 216) found that all individualist and subjectivist accounts of concept application clearly are in trouble [114], but nonetheless found a place for the psychological study of science.

We cannot in this article go deeper into the role of psychology and cognitive science for the study of science, but just mention one line of development that seems particular fruitful. This is an approach based on the historical study of conceptual revolutions in science (in the wake of Kuhn 1962) which focuses on the cognitive processes during paradigm shifts (see, e.g., Andersen, Barker and Chen 2006 and Thagard 1992; 2012).

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4.4.3 Information science

Information science studies how scientists and scholars use various information sources and contribute to the knowledge about and technologies for scientific infrastructures (e.g., information retrieval systems). The subfield of knowledge organization contributes knowledge about, for example, classification systems, indexing, social tagging, and ontologies (see further in Section 5.1). In bibliometrics, their research often is closely related to that of sociologists. Ingwersen et al. (2020), for example, demonstrated how researchers with conflicting views tend to publish in different committed scientific journals. A recent paper basing information retrieval and knowledge organization on the philosophy of science is Hjørland (2021). (See further about this field in Section 5.)

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4.4.4 Terminology studies

Terminology studies were founded by Eugen Wüster (1898-1977), an engineer with a strong interest in information science and knowledge organization (he was associated with the journal International Classification, now Knowledge Organization for a period from 1976). His theoretical views are discussed by Cabré Castellví (2003), demonstrating how this field also developed away from positivist principles. An overview of the field is provided in the series Handbook of Terminology the first volume of which was edited by Kockaert and Steurs (2015).

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4.4.5 Genre studies

Genre studies and composition studies is the study of academic writing and communication and its different genres. Prominent contributions include Bazerman (1988), Hyland (2000), Swales (2004) and Thelwall (2019).

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4.5 Conclusion of Section 4

Science is studied by many different fields and from many different perspectives. Each of these fields and perspectives contributes to the overall understanding of science. However, all these fields are influenced by different epistemologies and are depending on more overall conceptions of science.

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5. Scholarly communication and knowledge organization


5.1 Overview

Scientific and scholarly communication is an interdisciplinary field of research and teaching. It includes parts of information science and knowledge organization because they are about providing infrastructures for science in the form of institutions (like research libraries, archives, and museums), systems (like bibliographical databases, classification systems and ontologies) and processes (such as indexing, retrieving, and synthetizing scholarly research) [115]. Therefore, as mentioned in Section 4.4.3, information science and knowledge organization should also be considered part of science studies in the broad sense. The study of scholarly communication is institutionalized in, among other, library and information science (LIS) departments.

Scholarly communication may be considered from a broad, overall perspective on what have been termed “the information ecology”, and from the perspective specific topics, concepts, systems, and processes in this ecology. The overall information ecology has, for example, been modelled by “the UNISIST model” (UNISIST 1971; Fjordback Søndergaard, Andersen and Hjørland 2003) as a system of actors, institutions, systems, and processes in two dimensions:

  1. from knowledge producers to users with primary, secondary, and tertiary information services;
  2. via different communication channels (informal, formal published, formal unpublished and tabular channels).

The broad perspective includes the study of concepts such as data, information, knowledge, science, documents, relevance, media etc. as well as the theories in which these concepts form parts as related to science infrastructures.

The study of specific elements in the information ecology encompasses an exceedingly long range of concepts, systems, and processes. From the perspective of information science, two focal points can be emphasized: (A) information retrieval, searching and seeking, and (B) knowledge organization.

(A) Information retrieval research (IR) is today mainly (but not exclusively) a part of computer science and is involved in the construction of Internet search engines. Its main paradigm is based on statistical relations between term frequencies in documents, requests, and collections of documents and links between documents (and it should rather be termed “document retrieval”). In a broader sense IR includes other perspectives, including what is below termed “information searching”.

Information searching (or document searching, including literature searching, picture searching, music searching, people searching, data searching etc.) is about bibliographies and bibliographical databases, terminology, and search strategies (e.g., the use of controlled vocabulary vs. free text or the use of citation searching vs. term searching). It is traditionally an important activity in libraries and traditionally a core competency of librarians and information professionals. Today, it plays an important role in evidence-based practices, but also more broadly in what has been termed information literacy.

Information seeking studies is mainly descriptive studies of how people search for information rather than prescriptive norms for how search should be done.

Today science studies play only a limited role in these three fields. It is, however, the opinion of the present author that closer connection with science studies in the broad sense may improve these fields considerably [116].

(B) Knowledge organization (i.e., the description, classification and indexing of documents and concepts, metadata assignment, and the development of classification systems, ontologies, and other kinds of → knowledge organization systems). This field has a connection to the philosophical classification of the sciences, as well as to the philosophy of classification. However, like the fields mentioned in point A, knowledge organization needs to develop a closer relation to science studies [117].

A long range of specific topics includes [118]:

Each of these many topics are often studied in a fragmented way and in the absence of overall perspectives. The field therefore seems to be without clear formulated research programs and strategies. Also, there have been strong tendencies to separate “user-oriented” approaches from “systems-oriented” approaches although it seems clear that the purpose must be to study socio-technical infrastructures to improve them — or to help the users better to navigate in them. What documents should be found is neither a psychological issue nor a technological issue, but an epistemological issue: What should be retrieved as answers to a query is what is best supported by scientific research (see further Hjørland 2021). That scientific research often is controversial does not, however, makes any answer as good as any other answer, but just introduces new questions about how to evaluate research. Before we return to this issue in Section 5.4, let us consider how IT-developments have provided new conceptions of science.

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5.2 Information Technology’s (IT) influences on science

There have been claims of fundamental changes in science caused by the developments in IT, using terms such as “data-driven science”, “big data science”, “e-science” [146], “open science”, “the Fourth Paradigm” and “the end of theory”; for a broad overview see Dutton and Jeffreys (2010). Such developments can only be very selectively and briefly presented here. No attempt will be given to define and distinguish these labels or to construe a taxonomy of them. In general, the terms represent literatures containing a mixture of (a) important breakthroughs in IT which influence the way science is carried out; (b) attempts to apply perspectives from science studies; (c) problematic philosophical assumptions, often based on a naïve form of empiricism; and (d) much hype.

The mentioned labels reveal the important development in the quantity of available data and of new tools, such as text mining, deep learning, visualization, network analysis and much more. We are now living in “the petabyte age”, and the availability of these huge amounts of data for science may often provide a much higher inclusiveness for scientific analyses, and new tools for data analyses. Big Data may permit larger sample sizes, cheaper and more extensive testing, and continuous assessment of theories. Mazzocchi (2015, 1251-2) found:

Many valuable insights have been gained by applying this approach. In bioinformatics, for example, it has triggered a change in modeling strategies to obtain biological insights from experiments. The process of model building is driven by the massive amount of data produced and less dependent on theoretical presuppositions or hypotheses […]. The use of data analysis helps researchers to cope with the astonishing complexity of these systems, especially when large spatial and temporal scales are involved.

It seems important to distinguish between big data produced by scientists for specific purposes versus big data produced for other purposes, but also used for scientific purposes. In 2012 CERN announced that it had finally proven the existence of the Higgs boson. This was done by LHC (Large Hadron Collider), the world’s largest and most powerful particle collider, which generates up to 600 million collisions per second and produces 15 petabytes data per year. Big Data, distributed computing and sophisticated data analysis all played a crucial role in the discovery of the Higgs boson. In this case all the data were carefully constructed by the scientists for this specific task, and as Mazzocchi (2015, 1253) writes:

the discovery of the Higgs boson was not data-driven. The collider experiments were mostly driven by theoretical predictions: It is because scientists were attempting to confirm the Standard Model of elementary particles that the discovery of the Higgs boson—the only missing piece—could occur.

In other cases, however, data have not been produced for a specific task, but have been found. This raises the question of the quality of the data for the given purpose. As Geoffrey Bowker (2005, 184) said: “Raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked with care”. In contrast to science driven by theory and hypotheses (and by careful preparation of data) the concept “data driven science” has been suggested. It has even been suggested that science now have reached a stage of development which has been called “the end of theory” (Anderson 2008), where “the data deluge makes the scientific method obsolete”. Anderson stated (electronic source, no pages):

The scientific method is built around testable hypotheses. These models, for the most part, are systems visualized in the minds of scientists. The models are then tested, and experiments confirm or falsify theoretical models of how the world works. This is the way science has worked for hundreds of years.
Scientists are trained to recognize that correlation is not causation, that no conclusions should be drawn simply on the basis of correlation between X and Y (it could just be a coincidence). Instead, you must understand the underlying mechanisms that connect the two. Once you have a model, you can connect the data sets with confidence. Data without a model is just noise.
But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete. […]
There is now a better way. Petabytes allow us to say: ‘Correlation is enough.’ We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

Anderson (2008) has been cited at the least 298 times according to Web of Science on August 12, 2020. However, according to Norvig (2008) it was a provocation stating untrue claims, which even the author himself considered false. What is a reality, however, is the widespread use of the term data driven with its tendency to a return to an inductivist epistemology, which has widely been criticized, for example, by Frické (2015, 651), who argued that theory is needed in every turn and wrote: “Data-driven science is a chimera”.

Microsoft published an edited book (Hey, Tolle and Tansley 2009) which contains chapters about developments in scientific infrastructures and scholarly communication in different fields of science and about general issues. Each chapter takes as its point of departure the conception of “the fourth paradigm”, which as suggested by late computer scientist James Nicholas Gray and in the book (xvii-xxxi) transcribed from a talk. Gray’s central idea is that science has developed through four paradigms (Gray 2009, xviii; italics in original):

First paradigm: Thousand years ago, science was empirical and describing natural phenomena.
Second paradigm: Last few hundred years, theoretical branch, using models and generalizations.
Third paradigm: Last few decades, a computational branch, simulating complex phenomena.
Fourth paradigm: Today, data exploration (eScience), unified theory, experiment, and simulation
  • Data captured by instruments or generated by simulator.
  • Processed by software.
  • Information/knowledge stored in computer.
  • Scientists analyzes database/files using data management and statistics.
  • Gahegan (2020, 1-2), interpreted these paradigms further [147].

    A closer look at these “four paradigms” poses, however, a range of questions:

    • Is it true that empirical science is thousand years old, and that theoretical science is only few hundred years? Would the opposite generalization not come closer to the truth? Would it not be better to say that it was the Scientific Revolution from about the middle of 1500s that made science experimental and empirical? (Although, of course, both empirical and theoretical elements have been influential throughout the history of science).
    • Is the electronic revolution really a scientific revolution comparable with empiricism and rationalism? Bell (2009, xi) compared the Fourth Paradigm to the revolution caused by the invention of printing. But the inventing of printing does not appear among Gray’s four paradigms. Would that be a more adequate predecessor? Is it the media or the epistemologies, that are changing, or how is the interaction between epistemology and media? [148]
    • The model uses concepts such as “data”, “information” and “knowledge” without a proper examination of these concepts or an attempt to provide fruitful definitions. Because they are not explicated, it cannot be seen if, for example, data are understood from the problematic philosophical assumption that a datum is “a single, fixed truth, valid for everyone, everywhere, at all times” (Edwards 2010, 283). About the data concept see further in Frické (2019) and Hjørland (2018).
    • It may be recalled that the view proposed in the present article is that rationalism, empiricism, historicism, and pragmatism always and in all disciplines are interacting and competing paradigms (although at a given time or domain one will tend to be the most dominant). The implication of this view is that today’s e-science should also be viewed as a struggle between paradigms. Books such as Edwards (2010) about climate science and Leonelli (2016) about data-centric biology provide insights on such different paradigms in e-science, that are unfortunately absent in the Fourth Paradigm.

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    5.3 Do “data” displace academic documents?

    A separate question is about the changing relations between academic papers on the one hand and “data” on the other hand. This question is connected to the problem whether science is driven by hypotheses (implying primacy of papers) or by data (implying primacy of data). Ginsparg (2009, 190) found that “we should neither overestimate the role of data nor underestimate that of text”, while Goble and de Roure (2009, 144) opinioned: “datacentric science could be characterized as being about the primacy of data as opposed to the primacy of the academic paper or document [with reference to Erbach 2006]”.

    This view was, however, not what Erbach (2006, 221) [149] claimed, which was: “A view that makes data sets first class objects requires certain changes in publication and documentation practice, for example the records for projects and publications in e-science information systems should be extended with new fields ‘used_dataset’ and ‘generated_dataset’ and the record for datasets with a field ‘depends_on_dataset’”. Erbach thus did not suggest that academic papers are being supplanted by “data”.

    Schöpfel et al. (2020) argued that although data in themselves are not documents, data represented in information systems are always kinds of data documents (e.g., “data repositories”, “data studies” or “data sets”, all distinguished from academic papers like journal articles, including “data papers”). The authors also found that data documents are little cited compared to the citations received by journal articles. This may indicate either that even in “data-driven science” the journal article is considered more important than the data set [150] or it may indicate that in major data-driven fields like climate research, data may have a huge importance but live relatively independent of the scientific literature. This provides thoughts of Price’s (1970, 8) statement that science is “papyrocentric”, but that technology is “papyrophobic”, that scientific articles quote a lot of literature, but that technological magazines do not. The reason for this, Price suggested, is “If you want to make capital out technological discovery, the last thing you want is that open publication that [sic] determines intellectual privacy property for the sciences”. In the sciences, on the other hand, recognition and citations are the capital, because these are the indications that the research is known and found relevant. We describe below in Section 6 that many recent conceptualizations of science described an increasing commercialization. Could it be that many areas of science based on big data (e.g., meteorological research) are more like technologies and therefore relatively “papyrophobic”? Or at least that they consider publications a less important kind of capital? [151]

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    5.4 Conclusion: Epistemology as the basis for studying scholarly communication

    We have formerly quoted Frické (2015, 651), who argued that in science, theory is needed in every turn. We have further argued that all domains of knowledge can be understood as the competition between different epistemologies driven by empiricist, rationalist, historicist, and pragmatic assumptions. The same is therefore also the case in the study of scholarly communication.

    In the case of scholarly communication this means that every algorithm and system, every choice of terminology and every means of evaluation and synthetization research must be considered hypotheses in need of research. Also, the criteria from which all systems and processes must be constructed and evaluated (summarized by the concept “relevance”, cf. Hjørland 2010) are by their nature deeply rooted in epistemology: What researchers in one paradigm find important, may be considered unimportant by researchers in another paradigm (see Hjørland 2002). Therefore, the study of scholarly communication cannot avoid philosophical issues, but has to uncover in what way different positions influences the information ecology and how users can be helped to navigate given this condition.

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    6. Further developments in the concept of science


    6.1 Introduction

    The last decades has witnessed a bewildering of new concepts relating to science and its changing nature. There are many claims that science has changed in a profound way and the concept of “epochal breaks” was suggested by Nordmann, Radder and Schiemann (2011) [152]. The editors wrote in the introduction (p. 1): “the idea that there has been a transformation in the relation of science, technology, and society so profound that our received notions of ‘science’ have been superseded by something else” [153].

    There is much use of “catchwords” about recent developments in the conception of science, however [154]. Nordmann, Radder and Schiemann in their introduction suggested that three major transformations or breaks are: “entrepreneurial science” and “triple helix” (Section 6.2), “mode 2 research” (Section 6.3) and “technoscience”, “postmodern science” and “postnormal science” (Section 6.4); finally, “citizen science” and “big science” (not presented by Nordmann, Radder and Schiemann 2011) are introduced in respectively sections 6.5 and 6.6.

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    6.2 “Entrepreneurial science” and “triple helix”

    The concepts “entrepreneurial science” (Etzkowitz 1998) and “triple helix” (Etzkowitz and Leydesdorff 1998) where used by the authors to show how academia, industry, and government became intertwined in the pursuit of research agendas.

    Ziman (1996; 1996b; 1998; 2000, among other publications) debated this further, framing the discussion in terms of ‘postacademic science’ and the norms that guide it. He found (1996a, 751) that “basic” or “pure” science can only be defined sociologically and that “the social institution that has customarily fostered undirected research, without regard for its practical use is, is academia. In effect, what we call basic research is almost synonymous with the type of research traditionally carried out by universities” [155]. Ziman (1998, 167) argues that academic research must be replaced by a new mode:

    Once upon a time, universities set the pattern. 'Science' — pure, basic, or even to some extent applied — was equated with academic research, which diffused outwards as a bundle of social practices into other sectors of society. Now a new mode of knowledge production has emerged outside academia, and is percolating back into the 'science base'. This process seems irresistible. If universities are to continue as major sites for research, they too will have to adopt the new mode, systematically and wholeheartedly.

    Ziman (2000) reconsidered the Mertonian CUDOS norms (see above Section 4.3.1) in the light of recent changed conditions for science. He opined, for example that people cannot be communalistic if they are urged to turn their investigations into patents and intellectual property and that the norm of disinterestedness means that scientists should accurately evaluate the contributions of their contemporaries. However, in post-academic science, government agencies and industry are playing an increasing role in deciding not only what research should be conducted but also how it should be evaluated. On that basis Ziman suggested an alternative set of norms using the acronym PLACE:

    • Proprietary, and therefore not necessarily communal
    • Local, with researchers concentrating on technical problems that may not contribute to general understanding
    • Authority vested in a managerial hierarchy, not in the individual researcher
    • Commissioned to solve specific problems, not as a contribution to knowledge as a whole
    • Expert, with the scientist valued as an expert rather than a source of creativity

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    6.3 “Mode 2 research”

    The second break, involving “new production of knowledge”, emphasizes that there is a new social contract between science and society. Gibbons et al. (1994) introduced the now famous distinction between the old “mode 1” and the new “mode 2”. Mode 1 is described as traditional research, which is academic, investigator-initiated, and → discipline-based knowledge production. This kind of research still exists, but it is being displaced by “mode-2 research”, which by contrast is described as context-driven, problem-focused, and interdisciplinary. It involved multidisciplinary teams that worked together for short periods of time on specific problems in the real world. (Nordmann, Radder and Schiemann (2011, 7) found, however, that the term “mode 2” — like “technoscience” despite their widespread use lack proper definitions.)

    Nowotny, Scott and Gibbons (2001) is a later book written by three of the same authors as wrote Gibbons et al. (1994). The authors base their view on the relations between science and society on four pillars:

    • The nature of Mode-2 society. Science is no longer dominated by cause-effect relationships and the search for control and predictability. This seems to be related to post-modernist accounts, but the authors stresses that they believe in rational discourse in contrast to post-modernist protagonists.
    • The contextualization of knowledge in a new public sphere called “agora”. The boundaries between science and society are eroded and new actors, whom we do not traditionally connect with research are coming to the fore.
    • The concept of social robust knowledge takes over (from the concept of knowledge as reliable). This concept is claimed to be relational (but not relativist or absolute) and process oriented.
    • The emergence of socially distributed experts. It is not possible to act as an expert relying on scientific reputation, but (225): “It rests on its ability to orchestrate the many heterogeneous and context-specific knowledge dimensions that are involved”.

    Many have found that this book provides important thoughts about the development of science, and the concept agora has been emphasized. The book has also, however, been reviewed as being very unclear (see, for example, Danermark 2003).

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    6.4 “Technoscience”, “postmodern science” and “postnormal science”

    The third break, discussed by Nordmann, Radder and Schiemann (2011) refers to the term “technoscience”, which Bruno Latour (1987; 1993) and Donna Haraway (1997) popularized. This approach does not claim that that today’s technoscience is radically different from previous versions but advocates a different way of looking at science.

    Some researchers use the concept “postmodernism” to characterize developments in (the conception of) science. Forman (2007) argues that a perspective in technoscience, in which heterogeneous actors draw on conceptual and material resources to forge new kinds of entities, including technical artifacts coincides with postmodernity. However, various thinkers use the term technoscience differently. Latour (1987; 1993) and Haraway (1997) emphasize primarily that this concept enables new ways of acting and interacting, while Forman laments that science has become subservient to the realization of desired ends by any means necessary. Channell (2017) described the view that by the second half of the Twentieth century the long-held distinctions between science and technology were beginning to disappear and, in the place of two individual disciplines, there emerged a new concept that some have labeled technoscience (with a reference to Latour 1987).

    Nordmann, Radder and Schiemann (2011, 6) wrote:

    Forman does not introduce specific labels for the different ways of conceiving the relationship between science and technology. Though he attributes the current way of thinking to postmodernity, he does not speak of ‘postmodern science.’ That label has been used by others without catching on as of yet. For some, like Stephen Toulmin (1992), postmodern science is a program more than a reality. It is a kind of disunified science that recognizes a multiplicity of standpoints and respects local conditions. Others, like Jan C. Schmidt (2007), use postmodern science, or nachmoderne Physik, to designate research that draws on theories of complexity and self-organization rather than privilege isolable cause-effect relations. The identification, characterization, simulation, and ‘domestication’ of particular highly complex phenomena resembles a ‘new natural history’, as Arie Rip (2002) has pointed out.

    Post-modern science is also a concept that has been applied to the field of social informatics. Smutny and Vehovar (2020, 537) wrote:

    Despite certain tendencies towards convergence, the current article argues that SI [Social Informatics research] should be understood as a post-modern science. Whereas modern science is built on a homology of experts and universalism, postmodern science builds on a paralogy of researchers and pluralism […] instead of preferring a single viewpoint, as in modern science, where certain fields can be further constituted as formal disciplines, in SI it is more suitable to follow the approach of postmodern science and observe only a broad common discourse that includes a number of viewpoints. Doing so eliminates the modern science challenge of constantly (re)defining some fixed thematic and methodological boundaries to separate different areas. Indeed, the postmodern perspective concurs that, by its nature, SI is forced into constant reformation of its practices and aims.

    “Postnormal science” is a related concept associated with Silvio Funtowicz and Jerome Ravetz (1993; 2001). It deals with scientific inquiry in high stakes situations where the disciplinary knowledge of normal science needs to be extended. Nordmann, Radder and Schiemann (2011, 6-7) further wrote that the production of new forms of ignorance in the course of scientific and technological development has been said by Ulrich Beck, Anthony Giddens, and Scott Lash (1994) to give rise to a “second modernity” or “reflexive modernization”, which mobilizes novel approaches to both governance and the production of scientific knowledge in order to deal with the often unintended and unpredictable effects of modernization.

    Nordmann, Radder and Schiemann (2011, 7) continued the list [156]. It seems important, however, to consider that many of the described characteristics of these “epochal breaks” are on the margin of science. As Ravetz and Funtowicz (2015, 254) concluded their article about new forms of science:

    We must keep a sense of perspective. All these developments in digital-age science, especially those on the populist fringe, are very much on the margin. The vast majority of scientific work is done just as it has been, in large labs with traditional structures of quality control. The immediate problems facing the scientific communities, such as deceleration of growth, shifting of activity to the East, disentangling the publications system, and shoring up the quality assurance system, continue to require attention from all concerned. Creativity flourishes, in a spate of new inventions as well as in debates on the scientific problems bordering philosophy and theology. But this article is devoted to ‘new forms of science’, and so long as they are new they will by definition be on the margins, uncertain, indeed speculative in their future course. We might adopt the title of a recent prophetic book, Science 3.0 (Miedema, 2012), as the motto for our journey into this unknown and challenging future.

    The above presented new conceptions of science were based on Nordmann, Radder and Schiemann (2011), which focused on the “epochal break hypothesis”. In addition, in the former Section 5.2 we presented some new conceptions influenced by developments in information technology (IT) and in Section 6.5 we shall now consider “citizen science”, followed by “Big science” in Section 6.6.

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    6.5 Citizen science (“science 2.0”)

    Among the recent changes in science is the growth of “citizen science”, which was defined in the mid-Nineteen-nineties by Irwin (1995, 11) as a “form of science developed and enacted by citizens themselves”. Socientize Consortium (2013, 6) defined it as public engagement in the scientific research process in which citizens participate actively in different ways, with their intellectual effort or knowledge, tools, or resources. The main aim is to “co-create a scientific culture” and an exchange of understanding. Cohn (2008, 193) defined: “The term ‘citizen scientists’ refers to volunteers who participate as field assistants in scientific studies.” Cohn also termed it “science 2.0” although he emphasized that the phenomena is not new (3):

    Working with citizen scientists is hardly new. The practice goes back at least to the National Audubon Society’s annual Christmas bird count, which began in 1900. About 60,000 to 80,000 volunteers now participate in that survey. What is new is the number of studies that use citizen scientists, the number of volunteers enlisted in the studies, and the scope of data they are asked to collect.

    Silvertown (2009, 467) wrote:

    Two centuries ago, almost all scientists made their living in some other profession. […]
    The rise of science as a paid profession is a relatively recent phenomenon, dating from the later part of the 19th century. However, citizen scientists have never disappeared, particularly in sciences such as archaeology, astronomy and natural history, where skill in observation can be more important than expensive equipment. Today, most citizen scientists work with professional counterparts on projects that have been specifically designed or adapted to give amateurs a role, either for the educational benefit of the volunteers themselves or for the benefit of the project. The best examples benefit both.

    Citizen science is a way to do research that would not be possible to do if the costs of professional scientists should be the alternative. But it is more than that. It is also part of “the open science movement” and according to Bautista-Puig et al. (2019, 1) it “aims primarily to create a new scientific culture able to improve upon the triple interaction between science, society, and policy in the dual pursuit of more democratic research and decision-making informed by sound evidence”. The same publication analyses scientific output on citizen science using bibliometric techniques and Web of Science (WoS) data and shows that output in the area has grown since 2010, with a larger proportion of papers (66%) mentioned in social media than reported in other studies.

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    6.6 Big science

    The term big science refer to developments in (parts of) science since WW2. (Not to be confused with the term big data, which developed in the 1990’s, presented in Section 5.2). Big science was coined by Weinberg (1961), who wrote about the increase in funding for science and the high costs of research fields such as manned space flight and high-energy physics. His main emphasis was to address two questions: (1) Is big science ruining science? (2) Is big science ruining us financially? In the context of the present article, question 1 is important as it addresses qualitative changes in science influenced by its quantitative developments. He quotes [without providing a bibliographical reference] the English astronomer Fred Hoyle’s misgivings including the sentence “wherever science is fed by too much money, it becomes fat and lazy”. Hoyle claims to see evidence that the tight intellectual discipline necessary for science is, especially in America, being loosened, and Weinberg wrote that he shared Hoyle’s misgivings. Weinberg (1961, 161) wrote:

    [S]ince Big Science needs great public support it thrives on publicity. The inevitable result is the injection of a journalistic flavor into Big Science which is fundamentally in conflict with the scientific method. If the serious writings about Big Science were carefully separated from the journalistic writings, little harm would be done. But they are not so separated. […] When these trends are added to the enormous proliferation of scientific writing, which largely remains unread in its original form and therefore must be predigested, one cannot escape the conclusion that the line between journalism and science has become blurred.

    Although this specific claim about the development of big science was questioned by Panofsky (1992), Weinberg’s interests in the importance of big science for the healthy development of research seem important.

    Derek John de Solla Price (1963) contributed making the term big science popular (and well known in science studies, bibliometrics, and information science) by the title of his book Little Science, Big Science. The book is especially known for his empirically based claim that the growth of science has been exponential since the Seventeenth century (measured, for example, in terms of scientific workers, scientific publications and dollars spent on scientific work). Price used the term “science of science” about his research field. Regarding the concept “big science” he contrasted science in the Seventeenth century (“little science”) with science as it looked when he wrote the book (“big science”). Price claimed that “big science” is not just defined by its size, but also and perhaps primarily in terms of certain significant differences in quality, including, for example, its declining growth rate, its dominance by invisible colleges and its potential for driving far-reaching social change. Price’s book, its background and influences has been studied by, among others, Cole and Meyer (1985), Furner (2003a; 2003b) and Garfield (1985).

    Price’s observations contributed to the popularization of the term information explosion [157]. This, again, gave rise to increasing interest in “information storage and retrieval” and the establishment of “information science” as a discipline. The term has, however, been seriously criticized. Spang Hanssen (2001), for example, wrote that the explosion in the number of published documents should not be confused with an explosion in degree people are being informed. There is therefore no indication that demonstrates that Price’s growth patterns show a growth in information.

    One of the issues associated with big science is the increasing tendency to evaluate research by outsiders rather than by inside scholarly experts. This aspect is addressed by Chubin (1987), who focused on bibliometrics and quantitative “science indicators”. He rightly pointed out that Garfield’s (1979) sounding “yes” to his own question “Is citation analysis a legitimate evaluation tool?” smacks as much of self-interest as does scientists’ a priori resistance to that tool on the grounds that the outsiders are neither trained nor practitioners of X, therefore lack understanding of research in X and the ability to produce valid assessments of X. Chubin does not, however, follow-up on the consequences of such self-interests. It is not just Garfield, but the whole field of bibliometrics, which has such a self-interest. Correspondingly in many other fields: Psychologists may have a self-interest that psychological testing is valid and reliable, psychiatrist in the reliability of diagnoses of mental diseases etc. Therefore, scientists’ self-interests may always affect science, and this issue should be at the forefront in the management of big science and of research evaluation, but it seems to be neglected. The possible negative implications of big science for the internal soundness and quality of scientific inquiry were also addressed by Remington (1988).

    Crease and Westfall (2016) made a distinction between “old big science” and “new big science”. In the first a chief problem was narrow focus to justify the existence and cost of their tools. “In the new big science, a chief problem is diffuse focus. Machines are more diverse, and because they are used by an amorphous, ever-changing collection of users, research agendas are open-ended. As a result, managers and funders have had to develop new methods for handling, promoting, and evaluating research”. Westfall (2003) criticized the use of “big” and suggested alternatively the following set of terms: “Modest-“, “Mezzo-“ and “Grand Science”.

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    6.7 Conclusion of Section 6

    This section provided a selected overview of many new conceptions that have been suggested for understanding recent developments in the concept “science”. It is hard to form a clear conclusion of these developments, but the interpretation here is that most of these conceptions are based on the increasing commercialization of science, which many evaluate as a positive trend. In that way, these conceptions are connected to the pragmatic philosophy of science. However, as already described, pragmatic philosophy has an internal conflict between a long-term, objectivist understanding and a short-term subjectivist understanding. The ideas reported here in Section 6 seems to have failed to address the important question of how science can continue to penetrate ever deeper to understand the world, and not just reflect the more immediate social and commercial interests.

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    7. Overall conclusion

    This article has brought together a lot of fragmented knowledge about science from many different fields and perspectives. A main conclusion is that these different pieces need to work together in a much more integrated way. It is important to avoid too narrow a conception of science, and we may state with Stephen Toulmin (1963, 15):

    A nutshell definition of science — as of anything else — inevitably floats around on the surface. An investigation of any depth forces us to recognize that the truth is much more complex. To understand the ways in which […] scientific ideas differ, in any age […] calls for a painstaking and laborious study: only in this way shall we bring to light the manifold functions that science has performed, performs now, and might perform in the future within our whole intellectual economy.

    The most important lesson is that inductive, deductive, and abductive methodologies, bottom-up and top-down strategies of inquiry, should be considered iterative processes taking place in socio-historical and political contexts and “paradigms”, which cannot be ignored. In addition it must be emphasized that each tradition and each contribution is always dominated by certain philosophical assumptions, which need to be interpreted in light of other philosophies because we are dealing with what at the deepest level counts as relevant knowledge and information: All activities concerning science, from producing over mediating (retrieving, publishing, digitalizing, curating, translating, organizing, teaching etc.) to use must realize the socio-cultural and paradigmatic conflicts, which are necessarily involved in such activities.

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    Acknowledgments

    Thanks to Fulvio Mazzocchi for serving as editor and providing detailed and fruitful suggestions and to two anonymous reviewers who contributed much to the improvement of the article (and made me reconsider some issues, in particular in relation to the strong program in the sociology of science).

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    Endnotes

    1. The selected definitions of science in OED for this article are:
    4a. Paired or contrasted with art (see art n.1 3a). A discipline, field of study, or activity concerned with theory rather than method, or requiring the knowledge and systematic application of principles, rather than relying on traditional rules, acquired skill, or intuition. See note in etymology, and cf. etymological note at art n.1 In quots. a1387 and c1475 in uninflected plural form. In later use coloured by sense 4b.
    b. A branch of study that deals with a connected body of demonstrated truths or with observed facts systematically classified and more or less comprehended by general laws, and incorporating trustworthy methods (now esp. those involving the scientific method and which incorporate falsifiable hypotheses) for the discovery of new truth in its own domain. For more established compounds, as bio-, computer, geo-, life, natural, neuro-, physical science, see the first element.
    5a. The kind of organized knowledge or intellectual activity of which the various branches of learning are examples. In early use, with reference to sense 3a: what is taught in universities or may be learned by study. In later use: scientific disciplines considered collectively, as distinguished from other departments of learning; scientific doctrine or investigation; the collective understanding of scientists. Also with modifying word. In the 17th and 18th centuries commonly expressed by philosophy; cf. philosophy n. 5a.
    b. spec. The intellectual and practical activity encompassing those branches of study that relate to the phenomena of the physical universe and their laws, sometimes with implied exclusion of pure mathematics. Also: this as a subject of study or examination. Cf. natural science n. The most usual sense since the mid 19th cent. when used without any qualification. Often contrasted with religion when regarded as constituting an influence on a person's world view or belief system; cf. quot. 1967. Cf. also scientism n. 2.
    c. With the. The scientific principles or processes which govern or underpin a (specified) phenomenon, technology, etc. Also: the scientific research into these principles or processes. Usually with of or behind.
    d. Scientific results obtained from observations, experiments, etc.; scientific data. Frequently with the.

    2. There are no articles with the title “science” in Routledge Encyclopedia of Philosophy, in Stanford Encyclopedia of Philosophy or in the Internet Encyclopedia of Philosophy. There is one in Wikipedia (https://en.wikipedia.org/wiki/Science), which, however, fails to consider different conceptions of the term, and mostly seems to reflect a positivist view (ignoring constructivist and other critical views), which also seems to be the case with Encyclopedia Britannica (see below). The Handbook of Science and Technology Studies (Felt et al. 2017) also fails to discuss definitions and conceptions of science. The Encyclopedia of Empiricism contains an article “Science” (Downes 1997). Marxist oriented entries appear in Philosophisches Wörterbuch (Hörnig 1985, translated in Appendix 1) and in Europäische Enzyklopädie zu Philosophie und Wissenschaften (Juul Jensen 1990).
    Encyclopedia Britannica (online) on 2020-04-14 wrote: “Science, any system of knowledge that is concerned with the physical world and its phenomena and that entails unbiased observations and systematic experimentation. In general, a science involves a pursuit of knowledge covering general truths or the operations of fundamental laws. Science is treated in a number of articles.”

    3. OED on the term natural philosophy: ”Now chiefly historical. The study of natural bodies and the phenomena connected with them; natural science; (in later use) spec. physical science, physics”. Isaac Newton’s (1687) magnum opus, Principia Mathematica has the full title: Philosophiæ Naturalis Principia Mathematica. This demonstrates that natural philosophy was not a precursor of natural science, but just another name for it.

    4. OED on natural history: ”1. A work dealing with the properties of natural objects, plants, or animals; a systematic account based on observation rather than experiment. Now chiefly in the titles of books dealing with the wildlife of a particular region or the biology of particular organisms.
    2a. The facts relating to the natural objects, plants, or animals of a place; the natural phenomena of a region as observed or described systematically.
    b. In extended use: the details of any subject, esp. as regarded chronologically.
    3a. Originally: †the branch of knowledge that dealt with all natural objects, animal, vegetable, and mineral (obsolete). Now: the study of animals and other living organisms, esp. as presented in a popular rather than in a strictly scientific manner.
    †b. Things that form the subject matter of natural history. Obsolete. rare—1.”
    Kuhn (1962) used the term “natural history” about the preparadigmatic stage of science. When there is no organizing paradigm a field of science consists of unstructured and random fact gathering.

    5. Brock (2016, 2): “Until William Whewell coined the word 'scientist' in 1834, those who devoted all, or part, of their lives to the study of the natural world were referred to as 'natural philosophers'. By the 17th century, however, specialization had begun, and natural philosophy tended to refer to the more mathematical and quantitative interpretations of nature. Those involved in the study of plants and animals were said to practice natural history, and those studying the properties and reactions of different kinds of matter and their exploitation to improve the human condition, were referred to as chemists [or alchemists as the two kinds were often difficult to separate at that time].” (Until chemistry became part of natural philosophy with Robert Boyle (1626-1691) is was generally in low esteem.)

    6. Ross’s (1990) claim that scientists have left the more philosophical aspects of science to the philosophers may perhaps be questioned because scientists are important contributors to philosophy; take, for example, Albert Einstein, Niels Bohr, Thomas Kuhn as examples.

    7. Haack (1993a, 49) wrote that the term scientific is often used as an all-purpose term of epistemic praise, meaning “strong. Reliable, good”. This is caused by the impressing success of the natural sciences.

    8. Concerning the relations between science in the narrow meaning and philosophy, it is also relevant that the natural sciences generally became “independent” about 1850 and established their own “Facultatis Naturalis” (faculty of science). “Independence” in this connection meant autonomy and freedom from philosophical control and judgment.

    9. Mahner (2007, 543): “The factual and formal sciences, the technologies, and the humanities are all research fields producing genuine knowledge, which on the whole is either (approximately) true or else useful, and contributes to the understanding of the world and its inhabitants. For this reason, one might argue that they should all be included in a broad conception of science. This is for example done in the German intellectual tradition, where the name of almost any field of knowledge is dignified by the ending ‘-wissenschaft’ (-science), including the humanities, which are called Geisteswissenschaften (sciences of the mind). So there is bioscience alongside ‘music science’, just as there is computer science alongside ‘literature science’. Consequently, if a practitioner of a Geisteswissenschaft is told that what he does is not science, he will most likely be offended. It comes as no surprise that such a broad, if not inflationary, construal of ‘science’ aggravates the problem of demarcation (see, e.g., [Poser 2001]).
    By contrast, most other traditions and languages separate the arts and humanities from the sciences already terminologically, so that no offense is given by calling the humanities nonscientific. Yet even so, the question remains of what to do with mathematics and technology. While some authors include both of them in the sciences (e.g., [Kuipers 2001] classifies them as explicative research programs and design programs, respectively, within a broad conception of a scientific research program), others assert that neither mathematics [Lugg 1987] nor technology [Bunge 1983] are sciences. In any case, taking into account the preceding overview, the common post-positivist picture, which admits more categories than just sense (i.e., science) and nonsense (i.e., all the rest) […]. One the one hand, there is science including mathematics and technology; on the other there is nonscience including the arts and humanities as good nonscience, so to speak, for it too is viewed as producing true, reliable, or at least valuable knowledge, respectively, and finally pseudoscience as bad nonscience, for its knowledge claims are unjustified.”

    10. When history was established as a university discipline in USA in the spirit was “scientific”. Novick (1988, 25; italics in original): “Did late-nineteenth-century American historians, and especially that large portions of them who had studied in Germany, really think that Wissenschaft easily and naturally translated as ‘science’; that wissenschaftlich historical study meant the adoption of the (allegedly) purely empirical and neutral approach of the natural sciences? Such a suggestion beggars the imagination. Yet, as we shall see shortly, there is much to suggest that most historians believed something of the sort”.

    11. The most extreme conception of psychology as science is Watson (1913): “The behaviourist views psychology as a purely directive experimental branch of natural science”. During its history as a university discipline, different schools have clearly disagreed on the epistemological foundation of psychology (between positivism and hermeneutics, among others), and on its status as science or “studies”.

    12. Margolis (2009, x) described developments in his personal view as follows: “I see the larger themes in a more contested way than I had [in 1983 when he wrote the first edition of Culture and Cultural Entities]: the flux of the world as opposed to assured invariances; the historicity of thought as opposed to the universalizing aptitude of our cognitive faculties; the second-natured cultural transformation of our biological aptitudes as opposed to any mere biologism; the constructed nature of knowledge, perception, thought, science, and understanding as opposed to any pre-established correspondence between cognition and world; and now, more commandingly than ever, the ‘natural artifactuality’ of the self or person as opposed to the assumption that all the materials of the human sciences fit neatly within the scope and competence of the physical sciences. You may well hesitate before endorsing any of these notions, but you cannot doubt that they constitute a strong challenge to the most favored doctrines of the principal currents of Anglo-American philosophy down to our own day. I can only say that, for my part, I have followed the argument where it has led: what I’ve discovered (what I believe I’ve discovered) promises a sort of rapprochement among the principal movements of Western philosophy unwilling to yield on rigor but open to surpassing all the troubling stalemates of the preceding century”.

    13. A search in Web of Science, Core collection 2020-04-26 showed WC=ENVIRONMENTAL SCIENCES with 1,346,692 hits, with the most productive source titles being: Environmental science technology (41,025), Science of the total environment (36,403), Chemosphere (29,669), Water science and technology (23,712) and Atmospheric environment (22,375), whereas WC=ENVIRONMENTAL STUDIES gave 297,395 hits, with the most productive source titles being: Sustainability (19,623), Energy policy (12,995), Urban studies (7,939), Environment and planning A (7,208) and Regional studies (6,298).

    14. Sadegh-Zadeh (2015, 856-65) developed a 10-dimensional construct of science based on his own research and on Bunge (1983, 197 ff.). These dimensions were: 1. Community, 2. Society, 3. Domain, 4. Problems, 5. Goals, 6. Axiomatic basis, 7. Conceptual basis, 8. Methodological basis, 9. Deontic basis and 10. Research product.

    15. Born (1924) was the first publication in which the term Quantenmechanik (German for “quantum mechanics”) was used. However, many scientists were involved in developing this theory, both before and after 1924. See, for example, https://en.wikipedia.org/wiki/History_of_quantum_mechanics.

    16. Fuller (1998) claimed that scientists persist in accounting for themselves in terms of a common method despite the well-confirmed sociological perception that the various sciences share no common methods.

    17. Bauer (1992, vii) wrote: “Perhaps the central fallacy is that there exists an entity called ‘science’ about which sweeping generalizations can be made; for example, that science is characterized and defined by the scientific method (which, it is widely supposed, can be defined rigorously and unambiguously)”.

    18. Whewell’s position is described by Achinstein (2011, 350) as “holistic, since one most confidently infers the truth not of an isolated hypothesis, but of a system of hypotheses”. This is here understood as more related to the historicist and pragmatic positions but will not be further discussed in this paper.

    19. The ambiguities in the labels used for epistemological positions can be exemplified. The pragmatic philosopher William James (1912), for example, referred to his position as “radical empiricism”, while pragmaticism and empiricism in the present article are understood as fundamentally conflicting positions. Another example is Kuhn (1962), which according to the book itself criticized “positivism” and according to the “received view”, replaced it with “a historical turn”. However, one of the leading logical positivists, Rudolf Carnap, suggested that science is governed by “linguistic frameworks” in a way that corresponds to Kuhn’s “paradigms” (see Tsou 2015). If such an interpretation is correct, it either undermines the distinction between “positivism” (which may be understood as attempts to combine “empiricism” and “rationalism”) and “historicism” (and then challenges our suggested classification), or it makes the term positivism ambiguous. Our understanding is, however, that the distinction between “rationalism” and “empiricism” on the one side and “historicism” on the other side is important and fruitful (and the difference between Kuhn and Carnap’s positivism is supported by Tsou 2015). A third objection could be that the classification of Marxism as a version of pragmatism seems odd, but it is in the following based on the interpretation that they have the political dimension of knowledge in common. The choice of labels is in some way arbitrary, and the suggested classification could alternatively have been describing rationalism and three different forms of empiricism, but that alternative has not been preferred here and seems to agree with Kuhn’s criticism of positivism.

    20. Sosa (1998, abstract): ”Some foundationalists are rationalists who rely on intuition and deduction. Others are empiricists, in a broad sense, and accept observation and induction or abduction or yet other ways to support beliefs by means of other beliefs. What they have in common is that they are all willing to hazard a positive view about what in general makes a belief epistemically justified in the way required for it to be a case of knowledge; and they all propose something of the following general form: belief b is justified if and only if either b is foundationally justified through a psychological process of direct apprehension p (such as rational intuition, observation, introspection, and so on) or else b is inferentially justified through a psychological process of reasoning (such as deduction, induction, abduction, and so on) ultimately from beliefs all of which are acquired or sustained through p. If one rejects all forms of such foundationalism, then a question remains as to what distinguishes in general the cases where a belief is epistemically justified from the cases in which it is not. Can anything general and illuminating be said about what confers epistemic justification on a belief, and what gives a belief the epistemic status required for it to constitute knowledge (provided it is true)?”

    21. Levins and Lewontin (2009, 1-5) also described the Cartesian method as reductionist, “as a way of finding out about the world entails cutting it up into bits and pieces (perhaps only conceptually) and reconstructing the properties of the system from the parts of the parts so produced. But Cartesianism is more than simply a method of investigation; it is a commitment to how really things are [an ontological position]. The Cartesian reductionist method is used because it is regarded as isomorphic with the actual structure of causation […]. Cartesian reduction as a method has had enormous success in physics, in chemistry, and in biology, especially molecular biology”. Levins and Lewontin then explain the problems of generalizing this reductionist ontology and point to fields where this method is problematic and defend a dialectical method in which causation and explanation goes both from parts to wholes and from whole to parts.
    In this connection a trend towards post-reductionist science represented by the “complexity approach” should be mentioned, cf., Bechtel and Richardson (2010), Heylighen, Cilliers and Gershenson (2007) and Morin (2008).

    22. Achinstein (2011) based his description of Descartes’ rationalism on his Rules for the Direction of the Mind, English edition 1988 and Principles of Philosophy (Descartes 1971). Descartes’ Rules for the Direction of the Mind contained 21 rules (of 36 planned), of which the first 12 deal with his proposed scientific methodology in general. It was written about 1628 and was not published during the author's lifetime. The first Latin edition was published in 1701 (Regulae ad directionem ingenii). Achinstein (2011) cites from an English translation (Descartes 1988). The full English translation of the 21 rules are freely available in Wikisource: https://en.wikisource.org/wiki/Rules_for_the_Direction_of_the_Mind.

    23. Achinstein (2011, 346-7) wrote: “The view is a form of Rationalism according to which, although experience can suggest ideas to the scientist, whether these ideas are true can be known only by pure thought of a sort characteristic of mathematics. […] By ‘certainty’ Descartes does not mean ‘beyond reasonable doubt’ (by analogy with a criminal legal standard), but ‘beyond any possible doubt’ (as he envisages being the case in mathematics). […] the only way to obtain truth that is justified beyond any possible doubt is to employ what he calls ‘intuition’ and ‘deduction’. The former he characterizes as ‘the indubitable conception of a clear and attentive mind which proceeds from the light of reason. His examples include one’s thought that one exists, that one is thinking, that a triangle has three sides, and that 2 + 2 = 4. Their truth is immediately evident to us just by thinking them. By ‘deduction,’ Descartes means a continuous, uninterrupted train of reasoning to some proposition that follows necessarily from other propositions known with certainty. Example: an inference from 2 + 2 = 4 and 3 + 1 = 4 to 2 + 2 = 3 + 1”.
    (Concerning an alternative explanation of why 2+2=4 see Barnes, Bloor, and Henry 1996, chapter 7).

    24. It should be mentioned, however, that Edmund Husserl, the founder of the phenomenological tradition in philosophy, saw phenomenology as “First philosophy”, and as “an a priori science that proceeds from the first-person perspective and primarily aims at revealing essential structures of consciousness” (Berghofer and Wiltsche 2020, 3) and (p. 7): ”Essential laws can and must be immediately grasped; like certain mathematical truths they present themselves not to sensory intuition, but to categorial or eidetic intuition”. However, Husserl made a turn towards historicism; Berghofer and Wiltsche (2020, 11) wrote: “Yet, as the later Husserl came to realize, static phenomenology is but one possible approach, and a limited one at that. Instead of taking fully constituted objectivities as a starting point, one can also focus on the becoming of these objectivities, their ‘history of objectivation’, as Husserl puts it (Husserl 2001a, 634), and thus on the sedimented layers of constitution that underlie our experience of objects”.

    25. Hjørland (2013a and elsewhere) has argued that facet analysis and logical division are methods in classification based on rationalism. To this can be added that much ontology development in computer and information science also seems to be based on rationalism. This conclusion is reached, because these approaches (a) do not describe an empirical methodology (b) do not include a historical-cultural dimension (c) do not consider political analyses of values, goals, interests and consequences in their methodological principles.

    26. Bluhm and Borgerson (2011, 204; italics in original) wrote: “2.1 Two Traditions in Medicine. Modern medicine has inherited two competing approaches to the care of patients, rationalism and empiricism. These terms, taken from the medical literature, are not used in the standard philosophical senses. Rationalists in medicine, for instance, do not only reason from first principles. Rather, they emphasize the importance of empirical investigation into basic mechanisms of disease. (The designation “rationalist” was likely picked to highlight the role of reason in this approach.) Empiricists in medicine are thought to be interested in whether something works, regardless of causes or mechanisms. Again, the use of the terminology does not correspond to classic philosophical accounts of empiricism. The rationalist/empiricist debate in medicine is, in philosophical terms, better described as a debate between empiricist approaches to medicine at different levels. While empiricism (in the philosophical sense) prevails in medicine, there are vigorous ongoing debates about whether it is more appropriate to ask questions about basic mechanisms of disease at the micro-level (pathophysiology) or whether it would be better simply to investigate what works at the level of the average patient (as in RCTs)”.

    27. The first sentences in Buhr and Starke (1985, 1010, here translated): “Rationalism: Name for an epistemological position that isolates the rational level of cognition and assumes that only thinking (reason) can find the truth. Rationalism also seeks the criterion of truth in thought. It rejects the sensual level of cognition as deceptive and confused, unsuitable for actual cognition”.

    28. Musgrave and Pigden (2016, §2.2) wrote: “This is related to Duhem’s [1991] thesis that, generally speaking, theoretical propositions—and indeed sets of theoretical propositions—cannot be conclusively falsified by experimental observations, since they only entail observation-statements in conjunction with auxiliary hypotheses”.

    29. Nickles (2005) lists the following challenges which changed or ousted classical empiricism: (1) The linguistic turn; (2) The holistic turn; (3) Rejection of the analytic-synthetic distinction; (4) Rejection of the scheme versus content distinction by Donald Davidson; (5) Rejection of the correspondence theory of truth; (6) Rejection of the linear-foundational model of justification; (7) Anti-Kantian Kantianism; (8) Rejection by Karl Popper (1902-1994) and the positivists of the traditional identification of empiricism with inductivism; (9) Rejection of the imagist tradition that treats cognitive states or contents as little pictures before consciousness; (10) Rejection of "the myth of the given", by Sellars and others, the idea that subjective experience provides a special, direct, infallible, nonnatural connection of knowing mind to known world; (11) the failure of phenomenalism and sense datum theories of perception; and, more generally, (12) rejection of the whole Cartesian-Lockean conception of cognition and language; (13) The failure of attempts to define knowledge precisely as justified true belief; which inspired (14) externalism versus internalism in epistemology; (15) Recognition of the importance of tacit versus explicit knowledge (knowledge-how vs. knowledge-that) and of embodied knowledge, for example, skilled practices that we cannot fully articulate; (16) The feminist introduction of gender variables into epistemology; (17) Competing attempts to naturalize and socialize epistemology; (18) The postmodern critique of empiricism. Postmodernists, including Richard Rorty and radical feminists and sociologists, regard empiricism, epistemology in general, and, indeed, the entire Enlightenment project to replace a tradition-bound life. (A closely related article by the same author is available at: http://science.jrank.org/pages/9140/Empiricism-Twentieth-Century-Beyond.html.)

    30. A classical rationalist argument against empiricism is: Empiricism claims that all knowledge comes from experience. This claim is, however, either derived from experiences, in which case it may be wrong given other experiences, or it is not based on experiences. In the last case it confirms the rationalist claim about the existence of fundamental principles of knowledge that are not due to experience.

    31. Berghofer and Wiltsche (2020, 5) wrote: “Husserl broadens his criticism [of psychologism] to include classical empiricism as an ultimately self-refuting position. One of Husserl’s main arguments is that empiricism “destroys the possibility of the rational justification of mediate knowledge, and so destroys its own possibility as a scientifically proven theory” (Husserl [1900] 2001b, 59). Husserl’s point here is that empiricism does not allow for the possibility of immediately grasping substantial epistemological principles, including principles that would govern any form of inferential reasoning. As a consequence, mediate (i.e., inferential) justification and knowledge would be impossible if empiricism were true. It is interesting to note that one of the most vocal contemporary critics of empiricism, Laurence BonJour, makes basically the same point when he accuses empiricism of amounting to ‘intellectual suicide’ [BonJour 1998]”.

    32. Fleck (1979) is an English translation of a German book from 1935, which means that Fleck predated Kuhn (1962).

    33. Slife and Slife (2014, 576, italics in original): “The general point here is that empiricism is not a conception or method for mapping an objective reality; it is an ideology for illuminating various aspects of an interpreted reality. That this reality is interpreted is not necessarily negative. It is only negative if one accepts the prejudice against prejudice and then overlooks that this acceptance is itself a prejudice. All methods and epistemologies, in this sense, are interpretations of reality. What is pivotal from this perspective is not only being aware of this interpretation but also taking it into account when considering method outcomes, especially power and economic relations”.

    34. A basic idea of numerical taxonomy goes back to the French botanist Michel Adanson (1763), who suggested that equal weightage should be given to all the characters while classifying plants.

    35. Richards (2016, 124-5): “There are at least two sources of subjectivity in phenetics [or numerical taxonomy]. The first is in the choice and coding of characters: what gets identified as a character and how it gets coded requires judgment. And it isn’t clear that the notions of ‘unit of information’ and ‘unit character’ [as suggested by Sokal and Sneath 1963] are helpful in determining what counts as a character. A second source of subjectivity in phenetics is that different coefficients of similarity generate different OTUs [Operational Taxonomic Units] and ranks. There are three kinds of coefficients of similarity — association, correlation, and distance, each with multiple associated algorithms, and it is not clear why on purely observational grounds one coefficient of similarity or algorithm is better than another. Some algorithms may be easier to use, but that doesn’t seem satisfactory. Perhaps it is up to the judgment of individual systematists. If so, then isn’t phenetics subjective […]?”

    36. Fraassen (1980) called his version of empiricism “constructive empiricism” and is based on the criticism of logical positivism in the wake of Kuhn (1962).

    37. Aune (2009) is a defense of the empiricist view as opposed to rationalism. It partly accepts classical views of empiricism but does not consider the arguments by Kuhn (1962) or the pragmatic position, which is strange since he formerly wrote a book about rationalism, empiricism, and pragmatism (Aune 1970). Aune's revised empiricism (2009, 238) rejected two principles of classical empiricism: “Two assumptions once thought distinctive of a responsible empiricism must be firmly set aside. One is the assumption that our empirical knowledge or well-founded opinion must rest on a foundation of subjective experience. Not only does our empirical knowledge fail to rest on anything that deserves to be called a foundation, but the nature of our subjective experience is also, as I noted, quite questionable, generating on-going controversy among philosophers and even empirical scientists. The other objectionable assumption is that inherently unobservable objects are unknowable and cannot meaningfully be described or referred to”.

    38. Johansson (2021, 51) call his version of nominalistic empiricism. His book contains the chapter 3: “Empiricism from Ockham to van Fraassen”, which (48-51) ends with his own six component empiricist stance. However, his account seems not able to distinguish the epistemological positions in, for example, two schools of biological taxonomy: numerical taxonomy and Darwinian genealogical classification and is therefore not seen as a challenge to the classification of epistemologies suggested in the present article.

    39. The original quote from Edwards (2010, xvii, italics in original) was: “What keeps historians in business? Why do they keep on writing new accounts of, say, the French Revolution or the Second World War? Don’t we already know everything about those events? In fact we don’t. There is always more to learn about the past. Historians continually discover previously unknown documents, letters, drawings, photographs, artifacts, and other kinds of evidence that reveal new aspects even of history’s best-known episodes. On top of that, our perspective on the past keeps changing, for many reasons. We argue about how to interpret the evidence, finding flaws in earlier interpretations. And we keep changing. What we want to know about the past, what we hope to discover there, depends on who we are now”.

    40. As stated by Henri Poincaré (1905, 159): “It is often said that experiments should be made without preconceived ideas. That is impossible. Not only would it make every experiment fruitless, but even if we wished to do so, it could not be done. Every man has his own conception of the world, and this he cannot so easily lay aside. We must, for example, use language, and our language is necessarily steeped in preconceived ideas. Only they are unconscious preconceived ideas, which are a thousand times the most dangerous of all”.

    41. See Klee (1997, Chapter 7: The Revenge of Historicism) for a fine introduction to Kuhn’s theory.

    42. Richard’s quote continues (2016, 91-92): “Darwin reinterpreted homologies to be structural similarities due to common ancestry, and analogies to be functional similarities due to adaptation by natural selection. The former then, but not the latter, were a good guide to ancestry and genealogy. In his Origin, Darwin explicitly dismissed the value of analogies for classification: ‘It might have been thought (and it was true in ancient times thought) that those parts of the structure which determined the habits of life, and the general place of each being in the economy of nature, would be of very high importance in classification. Nothing can be more false. No one regards the external similarity of a mouse to a shrew, or a dugong to a whale, of a whale to a fish, as of any importance. These resemblances, though so intimately connected with the whole of life of the being, are ranked merely as “adaptive or analogical characters’ (Darwin 1859, 414). […] He [Darwin] was proposing a special similarity method based on the theoretical foundation of classification as the representation of the evolutionary tree”.

    43. Whether or not Darwin considered himself an empiricist, is a different story. There are indications that he felt that empiricism is such a strong ideology, that it was impossible to go up against it, and that he therefore claimed to follow the empiricism (inductionism) of Francis Bacon. In his autobiography he proclaimed that he worked “on true Baconian principles, and without theory collected facts on a wholescale scale” (here cited from Lennox 1997, 78-80).

    44. According to Kuhn’s theory of scientific paradigms Ptolemaic astronomers might learn the concepts “star” and “planet” by having the Sun, the Moon, and Mars pointed out as instances of the concept “planet” and some fixed stars as instances of the concept “star.” However, after a paradigm shift, Copernicans might learn the concepts “star”, “planet”, and “satellites” by having Mars and Jupiter pointed out as instances of the concept “planet”, the Moon as an instance of the concept “satellite”, and the Sun and some fixed stars as instances of the concept “star”. Thus, the concepts “star”, “planet”, and “satellite” got a new meaning and astronomy got a new classification of celestial bodies.

    45. Fjeldså (2013, 141) describe how many kinds of birds until very recently were considered blackbirds or subspecies of blackbirds: “Thus, rather than treating these blackbirds as different subspecies or as closely allied species, we can regard them as only convergently similar, as the males independently developed a black plumage, contrasting the yellow bill, as an effective means of demonstrating dominance within their territory. […] Nowadays, new data are being obtained at an intense rate. Many well-known and widespread ‘species’ have been found to have more complex population structures than had been assumed, and some may even represent a collection of different species that are only superficially similar”.

    46. Popper (1957) is a book criticizing historicism, as he understood the term. There is a general understanding that Popper used the term in a narrow and problematic way. About the reception and criticism of the book see the Wikipedia article: https://en.wikipedia.org/wiki/The_Poverty_of_Historicism. Popper rejected the inductive method (and thus classical empiricism and logical positivism), but he thought that theories could be falsified. This seems to conflict, however, with the insight in the theory-laden nature of observations. If the observation report: “this is a black swan” is theory-laden, then the observation does not falsify the theory “all swans are white”. We are now dealing with two theories about whether it is a swan, we observe, and how can we tell which one is correct? Therefore, Popper’s theory is not based on the same (hermeneutic) view as that of Kuhn. Perhaps the reader finds it absurd to suggest that the observation “this is a black swan” is theory laden. But in biological systematics, the definition of species is clearly theory-dependent, and as said elsewhere in this article, the concept “blackbird” has recently changed rather dramatically. So, whether the black bird you observe is a swan or not, is a theory. For a developed criticism of Popper’s position see Haack (2009), chapter 5: “The Evidence of the Senses: Refutations and Conjectures”.

    47. Realism is explained by Rescher (2006, 386): “Realism has two indispensable and inseparable components: the one existential and ontological, the other cognitive and epistemic. The former maintains that there indeed is a real world: a realm of concrete, mind-independent, objective reality. The latter maintains that we can to some extent secure adequate descriptive information about this mind-independent realm, and that we can validate plausible claims about some of the specifics of its constitution. This second contention obviously presupposes the first”.

    48. Compare Levi (2006, 384-5): “Scientific method is now conceived of as constituted by the background information, programs for routine expansion, and research programs that direct the demands for information that inquirers currently endorse. This ever-changing body of method is, indeed, self-correcting as compared to exclusive reliance on programs for routine expansion via consulting authorities or, for that matter, consulting only the testimony of the senses. But just as routine expansion via the testimony of the senses can on some occasions be a legitimate way of obtaining new information, so can the consultation with experts. The use of authorities judged to be reliable sources of information is surely vital to the success of scientific inquirers who must engage in a division of cognitive labor”.

    49. Hjørland (2005, 141-3, Part 4: Empiricism’s relation to literature and libraries (“read nature not books”) presented empiricism strange neglection of the role of literature and libraries in science, at the least implicitly.

    50. For a short overview of pragmatism see Legg and Hookway (2019). The inventor of pragmatism, Peirce (1905, 163), wrote “For this doctrine he [Peirce, speaking in third person about himself] invented the name pragmatism. Some of his friends wished him to call it practicism or practicalism (perhaps on the ground that πρακτικός [transcribed into the Latin alphabet as: praktikos] is better Greek than πραγματικός [transcribed into the Latin alphabet as: pragmatikos]). But for one who had learned philosophy out of Kant, as the writer, along with nineteen out of every twenty experimentalists who have turned to philosophy, had done, and who still thought in Kantian terms most readily, praktisch and pragmatisch were as far apart as the two poles, the former belonging in a region of thought where no mind of the experimentalist type can ever make sure of solid ground under his feet, latter expressing relation to some definite human purpose. Now quite the most striking feature of the new theory was its recognition of an inseparable connection between rational cognition and rational purpose; and that consideration it was which determined the preference for the name pragmatism”.

    51. One version of Marxism is Hörnig (1985) translated in Appendix 1. For a different Marxist interpretation of science based on Antonio Gramsci see Omodeo (2019). (Although Hörning is explicit about its Marxist perspective, he does not reveal on which specific interpretation it is based. This is probably the perspective developed by Nikolai Bukharin, also presented by Omodeo 2019).

    52. About critical theory and pragmatism see, for example, Ghiraldelli (2006).

    53. Haack (1993b) is an article by a philosopher who is both inspired by classical pragmatism and consider herself to be a feminist, but which is highly critical towards the concept “feminist epistemology” (and also towards politicized epistemology in general). A possible response to her arguments is that feminist epistemology is about general principles for research, illuminated by the following quote from Code (1998): “The impact of feminism on epistemology has been to move the question ‘Whose knowledge are we talking about?’ to a central place in epistemological inquiry. Hence feminist epistemologists are producing conceptions of knowledge that are quite specifically contextualized and situated, and of socially responsible epistemic agency”.

    54. Levi (2006, 378) wrote: ”Charles Peirce (see Peirce) and John Dewey (see Dewey) made the topic of inquiry the central problem of their pragmatic philosophies and both took inquiry to have the character of practical deliberation aimed at choosing policies suited to promoting the goals of deliberating agents. Unlike Dewey, Peirce thought that inquiry whose results is the fixing of belief ought to have goals that are distinct from the moral, political, economic, prudential, and aesthetic concerns that agents also have. Nonetheless, Peirce, like Dewey, thought of inquiry as seeking to realize some goal or solve some problem, and thought of the intelligent conduct of such goal-directed inquiry as analogous in this respect to practical thinking”.

    55. “Teleology means the explanation of phenomena in terms of the purpose they serve rather than of the cause by which they arise, thus pragmatic classification emphasizes the purpose the classification serves. Peirce (1902, EP II, 127) wrote: “All natural classification is then essentially, we may almost say, an attempt to find out the true genesis of the objects classified. But by genesis must be understood not the efficient action which produces the whole by producing the parts, but the final action which produces the parts because they are needed to make the whole. Genesis is production from ideas. It may be difficult to understand how this is true in the biological world, though there is proof enough that it is so. But in regard to science it is a proposition easily enough intelligible. A science is defined by its problem; and its problem is clearly formulated on the basis of abstracter science”.
    Bruhn Jensen (2021, 2ff.) discussed the epistemological views of aiming at considering “What is, what ought to be, and what could be” in inquiry.

    56. Pihlström (2017) wrote about values in pragmatism: “A key idea in Rescher’s axiology and metaethics is that the pragmatic principle of rational evaluation through purposive efficacy should be extended to the normative area. Values, no less than methods employed in factual belief-acquisition, ought to be pragmatically assessed; they are not just ‘matters of taste’. What is decisive in such assessment is the capacity of our values to contribute to the realization of human interests. Hence, philosophical anthropology is needed in the pragmatic legitimation and rational criticism of values”.

    57. For a fine historical overview of scientific method as a political and rhetorical issue, see Schuster and Yeo (1986).

    58. Johannessen and Olaisen (2005, 1261-2): “Science is for systemic thinking a moral project (Bunge, 1989). If science is not constructed as a moral project, it will not only lose its legitimacy but also its direction, which is the search for truth, and can thus be a means to achieve unethical goals”.

    59. The term political science has the standard meaning as the academic discipline studying politics but may also in some contexts be understood as politicalized science.

    60. Hacking (1999, 95-9), for example, discuss “the science war” where physicist Alan Sokal challenged social constructivists. He wrote: “In terms of the unmasking of established order, constructionists are properly put on the left. Their political attitude is nevertheless very much not in harmony with those scientists who see themselves as allies of the oppressed, but also feel like the special guardians of the most important truths about the word, the true bastions of objectivity. The scientists insist that in the end, objectivity has been the last support of the weak. Here is a disagreement: It is a rather messy matter, a sticky point involving deep-seated but ill-expressed attitudes. Who is on the left?” (See also Sokal and Bricmont 1998.)

    61. Wittich (1985, 967; translated from German) criticized James’ position from a Marxist point of view: “In this context, the difference between the Marxist-Leninist criterion of truth determined by practice and that of utility, as expressed by the idealistic philosophy of pragmatism, deserves attention. W. James (1907, 73) explains the truth criterion of utility, which he advocates, as follows: 'If it turns out that theological ideas are valuable for real life, they become true for pragmatism in the sense that they are ... useful'. In fact, statements of the type 'There is a God' or 'God has the property of being almighty, omniscient, omnipotent, etc.', are practically not used in this case, but rather statements of the type 'It is advantageous for a certain group of people (e.g. the ruling capitalist class) to claim that there is a god with this and this properties'. However, this statement is confirmed by the practice of class society as true and not only since W. James. At the same time, it clarifies the social function of religion. Pragmatic truthfulness therefore relates to the view that the truth of religious statements is advantageous for a certain group of people, but not to the religious statements themselves […].
    The development of the truth criterion of practice by Marxism gave the old materialistic doctrine of knowledge as an adequate reflection of objective reality a solid scientific basis“.

    62. Bernecker and Pritchard (2011) contains the following chapters about “kinds of knowledge”: 25. Inductive Knowledge, Alexander Bird; 26. A Priori Knowledge, Laurence BonJour; 27. Perceptual Knowledge, David Sosa; 28 Self-Knowledge, Sanford Goldberg; 29. Testimonial Knowledge, Jennifer Lackey; 30. Memory Knowledge, Sven Bernecker; 31. Semantic Knowledge, Peter Ludlow; 32. Scientific Knowledge, Peter Achinstein; 33. Logical and Mathematical Knowledge, Otávio Bueno; 34. Aesthetic Knowledge, Matthew Kieran; 35. Moral Knowledge, Robert Audi; 36. Religious Knowledge, Linda Zagzebski.

    63. Chandler and Munday (2016, electronic source, no pagination): “Unlimited semiosis: The term coined by Eco to refer to the way in which, for Peirce (via the interpretant), for Barthes (via connotation), for Derrida (via freeplay), and for Lacan (via ‘the sliding signified’; see slippage of meaning), the signified is endlessly commutable—functioning in its turn as a signifier for a further signified. In contrast, while Saussure established the general principle that signs always relate to other signs (see relational model), within his structuralist model the relationship between signifier and signified is portrayed as stable and predictable. See also difference”.

    64. Rescher (2006, 388): “bearing this pragmatic perspective in mind, let us consider this issue of utility and ask: What can this postulation of a mind independent reality actually do for us? The answer is straightforward. The assumption of a mind-independent reality is essential to the whole of our standard conceptual scheme relating to inquiry and communication. Without it, both the actual conduct and the rational legitimation of our communicative and investigative (evidential) practice would be destroyed. To be evidentially meaningful, experience has to be experience of something. And nothing that we do in this cognitive domain would make sense if we did not subscribe to the conception of a mind-independent reality. And since this is not a learned fact, then it is (and must be!) an assumption whose prime recommendation is its utility.”
    Further (393; italics in original): “(The ‘real world’ thus constitutes the object of our cognitive endeavors in both senses of this term — the objective at which they are directed and the purpose for which they are exerted.) And, further, reality is also to be seen as the ontological source of cognitive endeavors, affording the existential matrix in which we live and move and have our being, and whose impact upon us is the prime mover for our cognitive efforts”. Further (395-6): “We accordingly arrive at the overall situation of dual ‘retrojustification.’ All the presuppositions of inquiry are ultimately justified because a ‘wisdom of hindsight’ enables us to see that by their means we have been able to achieve both practical success and a theoretical understanding of our place in the world’s scheme of things. Here, successful practical implementation is needed as an extra-theoretical quality control monitor of our theorizing. And the capacity of our scientifically devised view of the world to underwrite an explanation of how it is that a creature constituted as we are, operating by the means of inquiry that we employ, and operating within an environment such as ours, can ultimately devise a relatively accurate view of the world is also critical for the validation of our knowledge”.
    Further (397; italics in original): “To be sure, this sort of idealism is not substantive but methodological. It is not a denial of real objects that exist independently of mind and as such are causally responsible for our objective experience. Quite the reverse: it is designed to facilitate their acceptance. But it insists that the justificatory rationale for this acceptance lies in a framework of mind-supplied purpose. For our mind-independent reality arises not from experience, but for it; that is, for the sake of our being in a position to exploit our experience to ground inquiry and communication with respect to the objectively real.
    Accordingly, what we have here is an object-level realism that rests on a presuppositional idealism at the justificatory infralevel. We arrive at a realism that is founded, initially at least, on a fundamentally idealistic basis. In sum, paradoxical though it may seem, we obtain a realism the tenor of whose justifying basis is thoroughly idealistic”.
    See also Pihlström (2014) for a broad discussion of the relation between pragmatism and realism.

    65. A problem for pragmatism is the distinction between fundamental research (science done without any intention to solve practical problems) and applied research (science done in order to solve specific problems, e.g. developing new medicines). It seems to be an implication of the pragmatic view that all science is applied science. It is a historical fact, however, that fundamental research often has had the greatest long-term importance, also from a pragmatic perspective. Peirce and other classical pragmatist were certainly interested in and contributed to fundamental research and committed to advancing scientific rationality and objectivity. As with many other kinds of dualisms pragmatism rejects a hard dichotomy between basic and applied science.
    However, it is increasingly a problem on how to manage this problem: Societies invests in science and higher education with clear expectations of benefit from this research (see also Hörnig 1985, Appendix 1). Societies must manage research in order to get useful knowledge, but on the other hand such initiatives may limit the possibilities of science to make fundamental progress because science that is subject to strong social, political and economic pressure may lose its critical role of “speaking truth to power” and to view problems from fundamental perspectives (see further Carrier and Nordmann 2011). Hörning (1985) from a Soviet-Marxist point of view claimed to have solved this problem but seems not to have. There are indications that attempts in the administration of science to increase its productivity and relevance may (at the least sometimes) be counterproductive. See, for example, Rodriguez-Navarro (2009). On the other hand, as public expenditure for research is now exceptionally large, it seems difficult keeping researchers on the pay-role without any demands. This problem seems hard to deal with, but its relation to pragmatism will not be further discussed in this article.

    66. Sarvimäki (1988, 58-9, italics in original) listed the following characteristics of the pragmatic theory of knowledge:
    “1. Man is primarily an actor, living and acting in a bio-physical, a socio-cultural and a subjective world.*
    2. Living and acting in the three worlds constitutes the a priori of human knowledge.
    3. Since living and acting constitutes the a priori of knowledge, knowledge is constructed in such a way that an application of well constructed knowledge will directly or indirectly serve living and acting.
    4. When knowledge becomes part of an acting system, it functions as an internal action determinant.
    5. There is a continuous interaction between knowledge and action so that knowledge is created in and through action and so that experiences that the actor acquires through action influences subsequent action.
    6. Value-knowledge, factual knowledge, and procedural knowledge are three types of knowledge connected to three types of internal action determinants. Having value-knowledge means knowing what fulfills the criteria of good values. Having factual knowledge means having true beliefs about the three worlds in which one is living. Having procedural knowledge means knowing how to carry out a specific act or act sequence.
    7. Knowledge can be unarticulated or articulated. Unarticulated knowledge is, for instance, tacit knowledge, familiarity, knowledge by acquaintance. Knowledge can be articulated in everyday language, science and art”.
    * Sarvimäki’s (1988) “three worlds” are here understood as metaphorical speech. There is one world, in which we may distinguish the bio-physical, the socio-cultural and the subjective world (compare Hjørland 2019).

    67. About “Philosophy of Praxis” see also Vogel (2017).

    68. Zalabardo (2019) considered two conflicting theories about how scientists ascribe predicates to things: (a) The primacy of reference theory (b) the primacy of practice theory. He wrote that it may be tempting to think of them in this way (183-4):
    “Pragmatic procedures are used in our day-to-day lay-person determinations of the representational status of predicates. The features they focus on are only contingently related to the representational status of predicates, so the procedures can produce false results. The referential procedure, by contrast, focuses on the facts that determine whether a predicate is representational. Hence, when correctly applied, the referential procedure produces infallible results, and can be used to validate the verdicts reached by pragmatic procedures. Applying the referential procedure is a job for the philosophers, who specialise in the language-world connections that the procedure focuses on. They will be able to determine whether the verdicts reached by lay people using pragmatic procedures are correct or incorrect”.
    For example, we may believe that something is gold by ascribing some predicates to it using our common-sense. However, a chemical analysis may say that it is not (ascribe other predicates to it). We may think that the chemical approach is not pragmatic (in the sense of theory-laden), and therefore infallible. But Zalabardo (2019) argues that it is not infallible, and that the referential procedure presumes that we are able to single out at least some properties directly, without the mediation of predicates or concepts, which (185) “strikes me as wildly implausible and I’m not even sure whether it has any contemporary advocates […] that the kind of unmediated access to properties that it contemplates is not to be had”.
    Unfortunately, although Zalabardo’s arguments are important they do not provide normative principles on how, according to the pragmatic view, inquiry should be done.

    69. Haack (2009) is a philosopher strongly influenced by classical pragmatism. She developed the view “foundherentism” as an alternative to both foundationalism and coherentism.

    70. A more comprehensive quote from Margolis (2009, x) is: “I see the larger themes in a more contested way than I had [30 years ago in the first edition of the book]: the flux of the world as opposed to assured invariances; the historicity of thought as opposed to the universalizing aptitude of our cognitive faculties; the second-natured cultural transformation of our biological aptitudes as opposed to any mere biologism; the constructed nature of knowledge, perception, thought, science, and understanding as opposed to any pre-established correspondence between cognition and world; and now, more commandingly than ever, the ‘natural artifactuality’ of the self or person as opposed to the assumption that all the materials of the human sciences fit neatly within the scope and competence of the physical sciences. You may well hesitate before endorsing any of these notions, but you cannot doubt that they constitute a strong challenge to the most favored doctrines of the principal currents of Anglo-American philosophy down to our own day”.

    71. To consider the study of science as part of natural sciences seems to be a contradiction in terms because science is a human and social activity. However, it has been suggested that bibliometrics is “the scientific method applied to science itself” (this view is probably widespread; the source here was an informal communication with a deceased bibliometrician, Finn Hjortgaard Christensen; see also Azoulay 2012).

    72. As Berghofer and Wiltsche (2020, 29) wrote: “To begin with, although Heidegger’s stance towards naturalism can generally be seen as somewhat ambiguous (cf. Rouse, 2005), he agrees with the Husserlian sentiment that the natural sciences are in principle incapable of investigating themselves in a philosophically satisfactory manner: ‘The moment we talk ‘about’ a science and reflect upon it, all the means and methods of this science in which we are well versed fail us’ (Heidegger 1967, 177). This is equally true of biology, where we ‘cannot put biology under the microscope’ (Heidegger 1967, 177), and of physics, which ‘itself is no [sic!] a possible object of a physical experiment’”.

    73. Gutting (2000, 463) found: “There is no doubt that philosophical accounts of scientific methodology aimed at telling scientists how to proceed with their work are today otiose. Such accounts made sense in the seventeenth century”.

    74. About unity of science, see, for example, Bertalanffy (1951) and Oppenheim and Putnam (1958).

    75. About the disunity of science, see, for example Dupré (1993), Fodor (1974) and Galison and Stump (1996).

    76. About natural versus human kinds, see, e.g., Khalidi (2013).

    77. Handbook of the Philosophy of Science (Gabbay, Thagard and Woods 2006ff) contains the following volumes:
    1. General Philosophy of Science: Focal Issues. Series Volume Editor: Theo Kuipers. Published 18th July 2007.
    2. Philosophy of Physics. Series Volume Editors: Jeremy Butterfield John Earman. Published 20th October 2006.
    3. Philosophy of Biology. Series Volume Editors: Mohan Matthen Christopher Stephens. Published 5th February 2007.
    4. Philosophy of Mathematics. Series Volume Editor: Andrew Irvine. Published 11th June 2009.
    5. Philosophy of Logic. Series Volume Editor: Dale Jacquette. Published 19th October 2006.
    6. Philosophy of Chemistry. Series Volume Editors: Robin Hendry Paul Needham Andrea Woody. Published 1st November 2011 (WorldCat has 2012).
    7. Philosophy of Statistics. Series Volume Editors: Prasanta S. Bandyopadhyay Malcolm R. ForsterPublished 25th May 2011
    8. Philosophy of Information. Series Editors: Dov M. Gabbay Paul Thagard John Woods. Published 10th November 2008.
    9. Philosophy of Technology and Engineering Sciences. Series Volume Editor: Anthonie Meijers. Published 20th August 2009.
    10. Philosophy of Complex Systems. Series Volume Editor: Cliff Hooker. Published 4th May 2011
    11. Philosophy of Ecology. Series Volume Editors: Bryson Brown Kevin de Laplante Kent Peacock Published 28th April 2011.
    12. Philosophy of Psychology and Cognitive Science. Series Volume Editor: Paul Thagard. Published 23rd October 2006.
    13. Philosophy of Economics. Series Volume Editor: Uskali Mäki. Published 23rd April 2012.
    14. Philosophy of Linguistics. Series Volume Editors: Ruth Kempson Tim Fernando Nicholas Asher. Published 14th January 2012.
    15. Anthropology and Sociology. Series Volume Editors: Stephen Turner Mark Risjord. Published 27th October 2006.
    16. Philosophy of Medicine. Edited by Fred Gifford. Published 21st July 2011.

    78. Among the textbooks on the philosophy of science Chalmers (1999) should be mentioned.

    79. Baur (2005, vol. 3: 1078, italics in original) wrote: “The term idealism in its broadest sense denotes the philosophical position that ideas (mental or spiritual entities) are primary and lie at the very foundation of reality, knowledge and morality, while non-ideal entities (such as physical or material things) are secondary and perhaps even illusory. Strands of idealistic thought can be found in ancient and medieval philosophy, but modern idealism begins in the wake of René Descartes (1596-1650), whose method of doubt problematized the relation of the mind (or spirit or ideas) to the material world and thus raised questions about how ideas ‘inside’ the mind can be known to interact with or correspond to any material, extended thing ‘outside’ the mind”.
    It is important to realize that both rationalism (as mentioned, e.g., by Descartes) and empiricism (in particular George Berkeley (1685-175) have strong idealist tendencies. This is the opposite of the popular belief that empiricism and positivism are materialist or realist positions.

    80. While Thomas Kuhn emphasized how our ontologies are implied by our theories and paradigms, he nevertheless emphasized that we cannot freely invent arbitrary structures: “nature cannot be forced into an arbitrary set of conceptual boxes. On the contrary […] the history of developed science shows that nature will not indefinitely be confined in any set which scientists have constructed so far” (Kuhn 1970, 263). The world provides “resistance” to our conceptualizations in the form of anomalies, i.e., situations in which it becomes clear that something is wrong with the structures given to the world by our concepts. In this way Kuhn’s view may be interpreted as (pragmatic) realism, although he is often interpreted as antirealist.

    81. Pragmatism is related to perspectivism, cf. Giere (2006) and Chang (2019). Teller (2019) discusses the relation between perspectivism and realism.

    82. About pseudoscience see, for example, Hansson (2017).

    83. About fringe science see, for example, Dutch (1982).

    84. About occult science see Hanegraaff (1996).

    85. On parapsychology see, for example, Hyman (2001; the 2015 edition of the same encyclopedia had no entry on this topic).

    86. On pathological science see, for example, Langmuir and Hall (1989).

    87. “Pre-paradigmatic science” was a concept developed by Kuhn (1962).

    88. On protoscience see, for example, Brakel (2000, 160-161).

    89. Pigliucci (2013) suggested “a family resemblance” clustering of sciences, which included concepts such as

  • “established science” (including particle physics, climate science evolutionary biology and molecular biology);
  • “soft science” (including economics, psychology and sociology);
  • “Proto- / quasi-sciences” (including search for extraterrestrial intelligence (SETI), string physics, evolutionary psychology, and scientific history);
  • “Pseudoscience”* (including Intelligent Design, astrology and HIV denialism**).

  • * Laudan (1984) used the label “pseudo-science” about “the strong programme” in the sociology of knowledge. This example indicates that what is considered “pseudoscience” depends on theoretical assumptions.
    ** HIV denialism is a contradictory set of claims without foundation in science that the human immunodeficiency viruses does not exist, or that it exists but is harmless, and that acquired immunodeficiency syndrome (AIDS) does not exist.

    90. Among the criteria suggested by Mahner (2013) is a look on the people involved and asking questions such as:

  • Do they form a research community, or are they just a loose collection of individuals doing their own thing?
  • Is there an extensive mutual exchange of information, or is there just an authority figure passing on his doctrines to his followers?
  • Is the given group of people free to research and publish whatever they want, or are they censored by the reigning ideology of the society they live in (e.g., Aryan physics, Lysenkoism)?
  • Does the domain of study consist of concrete objects, or does it contain fuzzy “energies” or “vibrations”, if not ghosts or other spiritual entities?
  • What are the philosophical background assumptions of the given field?
  • Does its ontology presuppose a natural, causal, and lawful world only, or does it also admit supernatural entities or events?
  • 91. Scientific taxonomy or classification in the sciences, such as, for example, biological classification of the species, has, contrary to classification of the sciences, met a growing interest among philosophers. Examples of contemporary philosophical contributions to scientific classification include Cooper (2017), Dupré (2001), Ereshefsky (2000), Richards (2016) and Wilkins and Ebach (2014) to mention just a few.

    92. Miksa (1998, 34-5) mentioned: “Those who participated included physicists and other scientists such as André-Marie Ampère, Neil Arnott, Wilhelm Wundt, and Karl Person, and a variety of philosophers of all kinds, such as C.-H. Saint Simon, Auguste Comte, Herbert Spencer, William Whewell, Thomas Masaryk, and Frederich Engels. The list could easily be extended. Footnote: Listings with discussions can be found in the work of E. C. Richardson (1930), Robert Flint (1904), and E. I. Shamurin (1955-59) [Miksa cited Russian edition and the spelling Shamurin, here the German edition, Samurin (1964) is cited]”.

    93. Often in older literature, “the classification of knowledge” and “the classification of the sciences” is used as synonyms, but today scientific knowledge should be considered one among more kinds of knowledge, and the two terms therefore should no longer be considered synonyms.

    94. The Cambridge History of Science (Lindberg and Numbers 2002-2020) begins chronologically with Ancient Mesopotamia and classical Greece and Rome, through the Medieval period, early modern Europe, and on through modern science and that approach continues on up to Vol. 4, after which the volumes split off into modern histories of different branches of science:
    Volume 1 (2018): Ancient Science, edited by Alexander Jones and Liba Taub;
    Volume 2 (2013): Medieval Science, edited by David C. Lindberg and Michael H. Shank;
    Volume 3 (2006): Early Modern Science, edited by Katharine Park and Lorraine Daston;
    Volume 4 (2003): Eighteenth-Century Science, edited by Roy Porter;
    Volume 5 (2002): The Modern Physical and Mathematical Sciences, edited by Mary Jo Nye;
    Volume 6 (2009): The Modern Biological and Earth Sciences, edited by Peter J. Bowler and John V. Pickstone;
    Volume 7 (2003): The Modern Social Sciences, edited by Theodore M. Porter and Dorothy Ross and
    Volume 8 (2020): Modern Science in National, Transnational, and Global Context, edited by Hugh Richard Slotten, Ronald L. Numbers and David N. Livingstone.

    95. Companion to the History of Modern Science (Olby et al. 1990)

    96. E.g., The Oxford Handbook of the History of Medicine (Jackson 2011)

    97. E.g., The Oxford Handbook of the History of Physics (Buchwald and Fox 2013).

    98. E.g., Danziger (1990): Constructing the Subject: Historical Origins of Psychological Research.

    99. An example of a recent history of the humanities is Bod (2013).

    100. For example, Richards (2016, 38) wrote: ”The essentialism story is misleading at best. The history of biological classification, beginning with Aristotle and continuing through to Darwin, is not a simple history of essentialist thinking about biological classification. It isn’t clear, for instance, that Aristotle or Linnaeus, the two arch essentialists in the essentialism story, were essentialists about biological taxa in the assumed way at all. Nonetheless, this essentialism story has been widely accepted, even in the face of contrary evidence from primary sources and the skepticism of various historians and philosophers”.

    101. Omodeo (2019, 2-3): “The socio-economic approach […] emphasized the collective character of science, the continuity between knowledge, production, and technology, as well as the concrete and practical dimensions of science. It explicitly opposed the bourgeois celebration of individual genius and the idealistic understanding of science as a purely intellectual endeavor, the progress of which purportedly depends on exceptional minds motivated by disinterested curiosity. This glaring opposition between the supporters of the externalist comprehension of science and the internalists, those who sought for the purity of scientific reason, can only be understood against the background of the cultural-political clashes of the Cold War. I will delve into this paradigmatic example at length in the book”.

    102. Zammito (2007, 802, notes omitted) wrote: ”The first compelling formulation of a ‘new’ sociology of knowledge emerged in the early 1970s. The intellectual energies for this ‘sociology of scientific knowledge’ (SSK) gathered primarily in Britain among thinkers inspired by Thomas Kuhn and Ludwig Wittgenstein, not trained in mainstream sociology. They proved willing to propose a most aggressive form of the ‘social construction of reality’ and to challenge the positivist tradition in its most sacred space: the privilege of natural scientific knowledge. They challenged not only the stablished field of sociology of science as developed by Merton and his followers, but also the Received View of the philosophy of science”.

    103. Downes (1998) wrote: “[C]onstructivists are accused of believing that scientists literally ‘make the world’, in the way some make houses or cars. This is probably not the best way to understand constructivism. Rather, constructivism requires only the weaker thesis that scientific knowledge is ‘produced’ primarily by scientists and only to a lesser extent determined by fixed structures in the world. This interprets constructivism as a thesis about our access to the world via scientific representations. For example, constructivists claim that the way we represent the structure of DNA is a result of many interrelated scientific practices and is not dictated by some ultimate underlying structure of reality. Constructivist research provides important tools for epistemologists specializing in the study of scientific knowledge”.

    104. Concerning a contemporary debate on biological classification and nomenclature see, for example, Sluys, Martens and Schram (2004); concerning debates on (a part of) the Periodical system see, for example, Vernon 2020.

    105. Latour and Woolgar (1986) is the second edition of Latour and Woolgar (1979). In this edition the word “social” was omitted from the title, which indicates a theoretical shift in Latour’s philosophy (an emphasis on non-social agents contribute to the construction of scientific facts). Hacking (1999, 39-40) discussed the redundancy of the term “social” and found: “But one need not agree with his [Latour’s] agenda in urging that we drop the ‘social’, except for an occasional emphasis”.

    106. Law (2009, 141) continued stating “it is possible to describe actor network theory in the abstract. I've just done so, and this is often done in textbooks. But this misses the point because it is not abstract but is grounded in empirical case studies. We can only understand the approach if we have a sense of those case studies and how these work in practice”.

    107. Among the issues taken up by Latour is the way scientist use bibliographical references in their papers. Here two theories are often described (1) “the normative theory of citing”, according to which citations are a way to acknowledge intellectual debts and, thus, are mostly influenced by the perceived worth, as well as the cognitive, methodological, or topical content of the cited articles and (2) the social constructivist’s “persuasion hypothesis”, according to which references are used to gain credibility by association (cf. Nicolaisen 2007). In general, social constructivism has been negatively considered in bibliometrics, and Nicolaisen’s paper claimed that the “persuasion hypothesis” is empirically rejected. However, two issues should be distinguished (1) that scientists behave in an unethical way, which Nicolaisen cite Latour for claiming and (2) that science is fundamentally influenced by interests (as described, e.g., by Barnes, Bloor and Henry 1996), which influences their perception of the value of different documents. The first meaning of social constructivism is problematic if understood as a norm (but may be true as a problematic practice by some authors in some cultures), while the second seems to be a fruitful hypothesis (cf. Hjørland 2002).

    108. Latour’s subjectivizing of nature is criticized by Omodeo (2019, 18) with reference to Latour (2014, 3) who describes the Earth as a full-fledged actor with emotions.

    109. Mirowski (2017, 447) wrote: “Latour believes politics consists of struggle without any hope of a transcendent court of appeal, which is why he is so attracted to figures like Hobbes, Walter Lippmann and Carl Schmitt. He explicitly eschews any appeal to Truth to ground politics, growing out of a conviction that constructivism dictates that truth is the outcome of struggle, but exhibits no special epistemic regularities or ontological stability. Because the portrait is one of unceasing agonistic strife, there is no program of reform, no conception of superior political institutional structures, no exemplar of political virtue to be found in his work. Science may be roiled with dispute and dissention from time to time, but the public just has to learn to roll with it. Most of all, there is nothing but ill-concealed contempt for those who strive to undertake science critique.40 The upshot of this Latourist project is that what exists in the way of science organization and scientific research is just fine the way it is”.

    110. “In A Treatise concerning the Principles of Human Knowledge (1710), George Berkeley argues that there is no external, material world; that houses, trees and the like are simply collections of “ideas”; and that it is God who produces “ideas” or “sensations” in our minds. This position has later been termed subjective idealism.

    111. Bloor (2015, 592-3) provides an explanation of why “The False Charge of Idealism [against the Strong Program]” has been so widespread. A main argument is that the contribution of “nature” versus “society” in scientific theories has often been understood as a zero-sum game — the more of the one ingredient meaning the less of the other. But this is wrong. It makes no sense to ask how much nature contributes and how much society contributes, just as it makes no sense to ask how much our visual experience is influenced by the object seen and how much by the eye, but it makes good sense to ask how the object and the eye influences our experience and how nature and society influences science.

    112. Small (2016, 49) wrote: “As someone trained in science and the history of science, the constructivist view did not ring true. Perhaps I was stuck in my story-book version of science. In any event, the bibliometrics community ignored the new sociology and remained largely empirical and atheoretical”. However, as argued in Hjørland (2016; 2020), there cannot be such a thing as atheoretical science or atheoretical empirical research. The bibliometrics community probably ignored the new sociology because they found it theoretically unfruitful, but instead of attempting to develop an alternative theory, they seem to have chosen to an atheoretical approach. If it is correct that an atheoretical approach is impossible, it follows that they have chosen an approach the implications of which is not examined. It should be added, that in spite of this criticism, Small’s contributions have been very important.

    113. The pluralist view of knowledge is the view that some phenomena require multiple accounts. Due to the complexity of the world and our representational limitations various models may be necessary, perhaps even incompatible models (cf. Kellers, Longino and Waters 2006). It is related to the idea of scientific perspectivism (see Giere 2006; Teller 2019; Chang 2019).

    114. Bloor (2007, 216) wrote: “[I]f a sociological model is on the right lines then all individualist and subjectivist accounts of concept application are clearly in trouble. Wittgenstein put his finger on the source of their weakness. Because, on these theories, there is no external standard outside the individual, then whatever seems right to the individual is right. But that, said Wittgenstein, means that one cannot talk about right in this case at all. There has got to be an external standard of right or wrong concept application and that standard is a social one”.

    115. Other parts of information science and knowledge organization is about broader cultural mediation, such as public libraries, but these parts are not unrelated to the scientific parts.

    116. About the importance of epistemology for information retrieval and searching see for example Hjørland (2011a), Hjørland (2016) and Hjørland (2021).

    117. An important book on philosophical issues related to metadata and data classification is Leonelli (2016), which is considering the field of (data-centric) biology. See also Ibekwe-SanJuan and Bowker (2017), Hjørland (2011b), Hjørland (2013b) and Hjørland (2021).

    118. Many of these specific elements sometimes claim to form independent sciences; for example, the study of journals has been called “journalogy”, cf., Lock (1989). The disciplinary boundaries are not well established in these fields.

    119. About academic genres see, for example, Swales (2004).

    120. About books, for example, Nunberg (1996).

    121. About bibliographies see, for example, Krummel (2017).

    122. About encyclopedias see, for example, Bergenholtz, Nielsen and Tarp (2009), Collison (1964), Fozooni (2012), König and Woolf (2013).

    123. About academic journals see, for example, Cole (2000); Cope and Phillips (2014); Kronick (1962); Lindsey (1978); Lock (1989); Morris et al. (2013).

    124. Concerning the study of journal articles see also the references mentioned in Section 4.4.5 Genre studies and composition studies.

    125. About patents see, for example, White (2017).

    126. About systematic reviews see, for example, Hammersley (2006).

    127. On standards, see, for example, Ransom et al. (2017).

    128. On publishing see, for example Baensch (2010) and Bhaskar (2013).

    129. On editing see, for example, Ginna (2017) and Butcher (2006).

    130. On peer-review see, for example, Lee et al. (2013).

    131. On open access see, for example, Albert (2006), Björk (2012).

    132. On predatory journals see, for example, Yeates (2017).

    133. About grey literature see, for example, Schöpfel and Farace (2010a; 2010b).

    134. About bibliometrics see, for example, Bellis (2009).

    135. About altmetrics see, for example, Thelwall (in press).

    136. About research evaluation see, for example, Moed (2005).

    137. About ranking of scientists and scientific journals see, for example, Andersen (2000).

    138. About the h-index see, for example, Bornmann, Mutz and Daniel (2008).

    139. About research libraries see, for example, Kennedy (2018).

    140. About archival science see, for example, Duranti and Franks (2015).

    141. About museology see, for example, Vergo (1989).

    142. About institutional repositories see, for example, Lynch (2003).

    143. Concerning memory institutions see, for example, Hjørland (2000).

    144. About research or knowledge syntheses see, for example, Cooper and Hedges (2009).

    145. About evaluation of information sources see, for example, Hjørland (2012) and Bailin and Grafstein (2010).

    146. On the developing concept of e-research see Jeffreys (2010).

    147. Gahegan (2020, 1-2, italics in original): “As described by Hey et al. (2009), science has evolved three main paradigms thus far. The first is Experimentation, characterized by observation and measurement. A good example is determining the relationship between the length of string and periodicity of a pendulum. Experimentation became possible with the invention of reliable ways to measure physical quantities, such as time, weight and length. Accurate measurement allowed observations to be standardized and compared, and so generalizations could be sought.
    The second paradigm is Analytical Theory, which searches for the theory that might explain some system. Note that the system does not need to be measurable, or even real, it can be hypothesized, so does not necessarily require data. Einstein’s famous equations describing special relativity fall into this category, and were motivated by thought experiments. The data to establish the validity of special relativity was not even available until after his death.
    The third paradigm is Numerical Simulation, characterized by the extensive application of computing power to model the physical world, often in great detail, and using forecasting techniques to predict future states. A good example is climate forecasting, combining simulation, historical data and prediction methods. Another is the search for nano-materials with interesting properties, using computational chemistry. Many newer branches of science now are home for research communities who work entirely on such in-silico experiments.
    The Fourth Paradigm, as proposed by Hey et al. (2009), is Data-Driven Science, and it goes further by suggesting that data itself can drive the discovery of new knowledge. Of all the current hype surrounding Big Data, this is perhaps the most intriguing aspect, with some authors even heralding: ‘The end of theory’ (Anderson, 2008). The argument goes that, with the availability of very rich data that comprehensively describes a given situation, it becomes possible to discover theoretically grounded explanations that make sense of, or explain, our observations”.

    148. This view is supported by Wilbanks (2009, 210-211), who — in the same book — suggested: “In this view, data is not a ‘fourth paradigm’ but a ‘fourth network layer’ (atop Ethernet, TCP/IP, and the Web [reference omitted]) that interoperates, top to bottom, with the other layers. This view seems to capture the nature of the scientific method a little better than the concept of the paradigm shift, with its destructive nature. Data is the result of incremental advances in empiricism-serving technology. It informs theory, it drives and validates simulations, and it is served best by two-way, standard communication with those layers of the knowledge network.
    To state it baldly, the paradigm that needs destruction is the idea that we as scientists exist as un-networked individuals. Now, if this metaphor is acceptable, it holds two lessons for us as we contemplate network design for scholarly communication at the data-intensive layer.”

    149. Erbach (2006) differentiated three views about research information:

  • The document-centric view: ” The traditional network analysis method in library science is bibliometrics, which studies authorship (author/publication) and citation (publication/publication) relationships to determine derived relations such as co-citation or impact factors. This approach will be referred to as the document-centric view of research information”.
  • The person-centric view: “In the context of research information, network analysis can be applied to determine relationships among people, based on their joint projects, organisations they work for, or data sets they work with. This is a person-centric view”.
  • The data-centric view: “Likewise, it [network analysis] can be used to analyse relationships among data sets based on the people who work with them, the publications that reference them, and the data analysis methods applied to them. This will be referred to as a data-centric view of research information”.

  • Consider, however, that the data-centric view is identical with the document-centric view if data sets are considered documents (as discussed by Schöpfel et al. 2020); to some degree this is also true for Erbach’s person-centric view: Bibliometrics, for example, shows relations between authors.

    150. This view seems supported by the conclusion reached by Lynch (2009, 183): “Further, I believe that in the practice of data-intensive science, one set of data will, over time, figure more prominently, persistently, and ubiquitously in scientific work: the scientific record itself”.

    151. Ravetz and Funtowicz (2015, 254) mentioned “disentangling the publication” as one of the immediate problems facing the scientific communities.

    152. Nordmann, Radder and Schiemann (2011) discuss the epochal break thesis, but in the epilogue (201), Radder found that there is a diversity of views on this thesis and to draw a single, straightforward conclusion for or against the epochal thesis would obviously be premature. Concerning the problem of defining the concept “epochal break” the editors also back away from suggesting one. They propose six “models” (8):

  • The epochal break between the medieval “dark” age and the renaissance with its light of reason
  • The Kuhnian scientific revolution or paradigm shift
  • “The so-called Hacking revolutions [… which] refer to a conceptual or technical innovation that can mark a point of no return” (the book provides no references to further information about this concept)
  • Forman (2007) “modeling the epochal break on the transition from modernity to postmodernity, by which he does not mean a break on the level of practice but on the level of ideology, interpretation, or cultural prestige”
  • “Media theorists refer to the epochal transition from analog to digital imaging, which severs the traditional causal chain from the original to its representation and allows any kind of data to be rendered in any number of visual forms”
  • “Michel Foucault’s notion of “epistéme” and a shift in the order of discourse—that is, in the presuppositions that accord power and efficacy to certain kinds of knowledge”.
  • 153. The received notion of science was described by Nordmann, Radder and Schiemann (2011, 1) as the “idea of science that values above all intellectual qualities like curiosity, creativity, and knowledge, and that [it] does so for the sake of the public rather than the corporate good”. What seems to replace this idea is mainly market driven research, which by some makes the distinction between “pure” and “applied” research morally bankrupt.

    154. Nordmann, Radder and Schiemann (2011, 7) wrote: “Some of these terms—‘technoscience’ and ‘mode-2 research’ in particular—will reappear throughout this book and to the astute reader, they may lack a proper definition. Indeed, more often than not, they are loosely descriptive of a phenomenon that remains to be fully understood. […] There is another reason why the chapters herein do not enter the thick of labels, adjudicating and comparing them one by one and one against the other. Rather than become entangled by them, it is important to reclaim the critical distance that allows us to ask what is at stake in these various descriptions and redescriptions of research practice”.

    155. This view was further explored in Ziman (1998), in which the term “fundamental research” was added as a synonym for “basic” science. This article discusses the problem of defining basic science and examined different possibilities: (a) a policy category (b) a cognitive category (c) basic science as an individualistic ideology (d) a sociological category and concluded (166):
    “We set out originally on a hunt for basic research. We thought of it as cognitively fundamental, and vocationally pure. What we have ended up with is academic research — all too often pedantic, sectarian, and corrupted with careerism. These are harsh words, but they reflect the exasperation of decision makers trying to develop a rational policy for the support of science. They insist, again and again, that they value basic research and must rely on the scientific community to conduct it for the common good. And yet the failure of academic research to live up to their expectations impels the policy makers to intervene more and more deeply in the detailed running of the science base”.

    156. Nordmann, Radder and Schiemann (2011, 7) continued: “These catchwords are by no means the only ways by which various authors seek to express what they perceive to be distinctive of much contemporary research. In the 1970s already, Gernot Böhme, Wolfgang Krohn, Wolfgang van den Daele (1973), and Wolf Schäfer (1983) spoke of ‘finalized science’. Once the business of internal theory development has been finished, research needs to orient itself explicitly toward specific social or technical ends that are to be achieved. Much more recently, Peter Galison (2006) [published as Galison 2018] began speaking of an ‘engineering way of being in science’ that is characterized by ‘ontological indifference’, while Ann Johnson (2009) employs the notion of ‘research in a design mode’. These terms capture the fact that many current research activities are more concerned with building or making than with knowing. Media theorists, art historians, and philosophers of modeling each from their own disciplinary perspectives ask whether there has been a major shift in the representational practices of science. And so, the list can be continued”.

    157. The term “the information explosion” has been traced to Lars Heide 1941, cf. Vahrenkamp (2017, 41).

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    References

    Abrahamsen, Knut Tore. 2003. “Indexing of Musical Genres. An Epistemological Perspective”. Knowledge Organization 30, no. 3-4: 144-69.

    Achinstein, Peter. 2011. “Scientific Knowledge”. In The Routledge Companion to Epistemology, edited by Sven Bernecker and Duncan Pritchard. New York: Routledge, 346-57.

    Adanson, Michel. 1763. Familles des plantes. Paris: Vincent.

    Agassi, Joseph. 2008. Science and Its History: A Reassessment of the Historiography of Science. Boston Studies in the Philosophy and History of Science 253. Dordrecht: Springer.

    Albert, Karen M. 2006. “Open Access: Implications for Scholarly Publishing and Medical Libraries”, Journal of the Medical Library Association 94, no. 3: 253-62.

    Alvargonzález, David. 2013. “Is the History of Science Essentially Whiggish?” History of Science 51, no. 1: 85-99. https://doi.org/10.1177/007327531305100104.

    Andersen, Hanne, Peter Barker and Xiang Chen. 2006. The cognitive structure of scientific revolutions. Cambridge, UK: Cambridge University Press.

    Andersen, Heine. 2000. “Influence and Reputation in the Social Sciences — How much do Researchers Agree?” Journal of Documentation 56, no. 6: 674—92.

    Anderson, Chris. 2008. “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete”, Wired 16, no. 7 (electronic source, no pages) http://www.wired.com/science/discoveries/magazine/16-07/pb_theory.

    Aune, Bruce. 1970. Rationalism, Empiricism, and Pragmatism. Atascadero, CA: Ridgeview.

    Aune, Bruce. 2009. An Empiricist Theory of Knowledge. Montague, Mass.: Bowler Books.

    Azoulay, Pierre. 2012. “Turn the Scientific Method on Ourselves”. Nature (London) 484, no. 7392: 31-32.

    Baensch, Robert E. 2010. “Publishing Studies”. In Encyclopedia of Library and Information Sciences. 3rd. ed., eds. Marcia J. Bates and Mary Niles Maack. Boca Raton, FL: CRC Press, vol VI, 4372-9.

    Bailin, Alan and Ann Grafstein. 2010. The Critical Assessment of Research: Traditional and New Methods of Evaluation. Oxford: Chandos Publishing.

    Barnes, Barry. 1977. Interests and the Growth of Knowledge. London: Routledge and Kegan Paul.

    Barnes, Barry, David Bloor and John Henry. 1996. Scientific Knowledge: A Sociological Analysis. Chicago: The University of Chicago Press.

    Barseghyan, Hakon, Nicholas Overgaard and Gregory Rupik. 2018. Introduction to History and Philosophy of Science. Licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted. Powered by Pressbooks. Toronto: University of Toronto Open E-text. http://hakobsandbox.openetext.utoronto.ca/.

    Bauer, Henry H. 1992. Scientific Literacy and the Myth of the Scientific Method. University of Illinois Press.

    Baur, Michael. 2005. “Idealism”. In The New Dictionary of the History of Ideas. New York: Charles Scribner's Sons, Vol. 3: 1078-82.

    Bautista-Puig, Núria, Daniela De Filippo, Elba Mauleón and Elías Sanz-Casado. 2019. “Scientific Landscape of Citizen Science Publications: Dynamics, Content and Presence in Social Media”. Publications 7, no.1: article 12; doi:10.3390/publications7010012.

    Bazerman, Charles. 1988. Shaping Written Knowledge: The Genre and Activity of the Experimental Article in Science. Madison, USA: University of Wisconsin Press.

    Bechtel, William and Robert C. Richardson (2010). Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research. Cambridge, MA: MIT Press.

    Beck, Ulrich, Anthony Giddens, and Scott Lash. 1994. Reflexive Modernization: Politics, Tradition, and Aesthetics in the Modern Social Order. Stanford: Stanford University Press.

    Bell, Gordon. 2009. “Foreword”. In The Fourth Paradigm: Data-intensive Scientific Discovery, eds. Tony Hey, Kristin Michele Tolle and Stewart Tansley. Redmond, VA: Microsoft Research, xi-xv. Published OA at: http://research.microsoft.com/en-us/collaboration/fourthparadigm.

    Bellis, Nicola De. 2009. Bibliometrics and Citation Analysis. From the Science Citation Index to Cybermetrics. Lanham, Maryland: The Scarecrow Press.

    Bentz, Valerie Malhotra and Jeremy J. Shapiro. 1998. Mindful Inquiry in Social Research. London: SAGE Publications.

    Berghofer, Philipp and Harald A. Wiltsche. 2020. “Phenomenological Approaches to Physics: Mapping the Field”. In Phenomenological Approaches to­ Physics, eds. Harald­ A. ­Wiltsche and Philipp ­Berghofer. Synthese Library 429. Cham, Switzerland: Springer.

    Bergenholtz, Henning, Sandro Nielsen, and Sven Tarp. 2009. Lexicography at a Crossroads: Dictionaries and Encyclopedias Today, Lexicographical Tools Tomorrow. Bern: P. Lang.

    Berkeley, George. 1710. A Treatise concerning the Principles of Human Knowledge. Dublin: printed by A. Rhames for J. Pepyat.

    Bernecker, Sven and Duncan Pritchard. Eds. 2011. The Routledge Companion to Epistemology. New York: Routledge.

    Bertalanffy, Ludwig von. 1951. General System Theory: A New Approach to Unity of Science. Baltimore, MD: John Hopkins Press.

    Bhaskar, Michael. 2013. The Content Machine: Towards a Theory of Publishing from the Printing Press to the Digital Network (Anthem Publishing Studies). London, UK: Anthem Press.

    Björk, Bo-Christer. 2012. “The Hybrid Model for Open Access Publication of Scholarly Articles: A Failed Experiment?” Journal of the American Society for Information Science and Technology 63, no. 8: 1496-1504.

    Bloor, David. 1976. Knowledge and Social Imagery. London: Routledge & Kegan Paul. (A second edition was published in 1991.)

    Bloor, David. 1982. “Durkheim and Mauss Revisited: Classification and the Sociology of Knowledge”. Studies in History and Philosophy of Science 13, no. 4: 267-97.

    Bloor, David. 1991. Knowledge and Social Imagery.2nd. ed. Chicago: Chicago University Press.

    Bloor, David. 1999a. “Anti-Latour”. Studies in History and Philosophy of Science 30, no. 1: 81—112.

    Bloor, David. 1999b. “Reply to Bruno Latour”. Studies in History and Philosophy of Science 30, no. 1: 131—36.

    Bloor, David. 2007. “Ideals and Monisms: Recent Criticisms of the Strong Programme in the Sociology of Knowledge”. Studies in History and Philosophy of Science 38, no. 1: 210—34. doi:10.1016/j.shpsa.2006.12.003

    Bloor, David. 2015. “Strong Program”. In International Encyclopedia of the Social and Behavioral Sciences 2nd, ed. James D. Wright. Amsterdam: Elsevier, vol. 23: 592-97. http://dx.doi.org/10.1016/B978-0-08-097086-8.85033-3.

    Bluhm, Robyn and Kirstin Borgerson. 2011. “Evidence-Based Medicine”. In Handbook of the Philosophy of Science. Volume 16: Philosophy of Medicine, ed. Fred Gifford. Amsterdam: Elsevier/North Holland, 203-38.

    Bod, Rens. 2013. A New History of the Humanities: The Search for Principles and Patterns from Antiquity to the Present. Translated from the Dutch by Lynn Richards. Oxford, UK: Oxford University Press.

    Böhme, Gernot, Wolfgang Krohn, and Wolfgang van den Daele. 1973. “Die Finalisierung der Wissenschaft”. Zeitschrift für Soziologie 2, no. 2: 128—44.

    Boghossian, Paul and Timothy Williamson. 2020. Debating the A Priori. Oxford, UK: Oxford University Press.

    BonJour, Laurence. 1998. In Defense of Pure Reason. A Rationalist Account of A Priori Justification. Cambridge: Cambridge University Press.

    Boole, George. 1854. An Investigation of The Laws of Thought on Which Are Founded the Mathematical Theories of Logic and Probabilities. Originally published by Macmillan, London. Reprint by Dover, 1958.

    Born, Max. 1924. “Über Quantenmechanik”. Zeitschrift für Physik 26, no. 6: 379—95. https://doi.org/10.1007/BF01327341.

    Bornmann, Lutz, Ruediger Mutz and Hans-Dieter Daniel. 2008. “Are There Better Indices for Evaluation Purposes than the h Index? A Comparison of Nine Different Variants of the h Index Using Data from Biomedicine“. Journal of the American Society for Information Science and Technology 59, no. 5: 830-7. DOI: 10.1002/asi.20806

    Bourdieu, Pierre. 2004. Science of Science and Reflexivity. Oxford: Polity. (Translated from Science de la science et réflexivité by Richard Nice. Paris: Raisons d'agir, 2001).

    Bowker, Geoffrey C. 2005. Memory Practices in the Sciences. Cambridge, MA: MIT Press.

    Brakel, Jaap. 2000. Philosophy of Chemistry: Between the Manifest and the Scientific Image. Leuven Univ Press.

    Brock, William H. 2016. The History of Chemistry: A Very Short Introduction. Oxford, UK: Oxford University Press.

    Bruhn Jensen, Klaus. 2021. A Theory of Communication and Justice. Milton Park, UK: Routledge.

    Buchwald, Jed Z. and Robert Fox. 2013. The Oxford Handbook of the History of Physics. Oxford, UK: Oxford University Press.

    Buhr, Manfred and Manfred Starke. 1985. “Rationalismus”. In Philosophisches Wörterbuch, edited by Georg Klaus and Manfred Buhr. Westberlin: Das Europäische Buch, 2: 1010-12.

    Bunge, Mario. 1983. Treatise on Basic Philosophy, Volume 6. Epistemology & Methodology II: Understanding the World. Dordrecht: D. Reidel Publishing Company.

    Bunge, Mario. 1989. Treatise on Basic Philosophy, Volume 8. Ethics: The Good and the Right. Dordrecht: D. Reidel Publishing Company.

    Butcher, Judith. 2006. Butcher's Copy-editing: The Cambridge Handbook for Editors, Copy-editors and Proofreaders. 4th ed. Cambridge, UK: Cambridge University Press.

    Cabré Castellví, Maria Teresa. 2003. “Theories of Terminology: Their Description, Prescription and Explanation”. Terminology 9, no. 2: 163-99.

    Caputo, John D. 2018. Hermeneutics: Fact and Interpretation in the Age of Information. London: Penguin.

    Carr, Patrick L. 2015. “Serendipity in the Stacks: Libraries, Information Architecture, and the

    Problems of Accidental Discovery”. College & Research Libraries 76, no. 6: 831-42. doi:10.5860/crl.76.6.831

    Carrier, Martin and Alfred Nordmann. Eds. 2011. Science in the Context of Application. Heidelberg, Germany: Springer.

    Chalmers, Alan F. 1999. What is This Thing Called Science? , 3rd ed. Buckingham: Open University Press.

    Chandler, Daniel and Rod Munday. 2016. A Dictionary of Media and Communication, 2nd. ed. Oxford, UK: Oxford University Press. (Online via Oxford Reference Premium Collection).

    Chang, Hasok. 2019. “Pragmatism, Perspectivism, and the Historicity of Science”. In Understanding Perspectivism: Scientific Challenges and Methodological Prospects, eds. Michela Massimi and Casey D. McCoy. New York, NY: Routledge, 10-27. Freely available at: https://doi.org/10.4324/9781315145198.

    Channell, David F. 2017. History of Technoscience: Erasing the Boundaries between Science and Technology. Book Series: History and Philosophy of Technoscience, Volume: 12. Oxford, UK: Routledge.

    Chubin, Daryl. E. 1987. ”Research Evaluation and the Generation of Big Science Policy”. Knowledge: Creation, Diffusion, Utilization 9, no. 2: 254-77.

    Code, Lorraine. 1998. “Feminist Epistemology”. In Routledge Encyclopedia of Philosophy, ed. Edward Craig. London: Routledge, 3: 597-602.

    Cohn, Jeffrey P. 2008. “Citizen Science: Can Volunteers Do Real Research?“ BioScience 58, no. 3: 192-7.

    Cole, Stephen. 1992. Making Science: Between Nature and Society. Cambridge, MA: Harvard University Press.

    Cole, Stephen. 2000. “The Role of Journals in the Growth of Scientific Knowledge”. In The Web of Knowledge: A Festschrift in Honor of Eugene Garfield, eds. Blaise Cronin and Helen Barsky Atkins. Medford, NJ: Information Today, 109-42.

    Cole, Stephen. 2004. “Merton's Contribution to the Sociology of Science”. Social Studies of Science 43, no 6: 829-44.

    Cole, Stephen and Gary S. Meyer. 1985. “Little Science, Big Science Revisited”. Scientometrics 7, nos. 3-6: 443-58. DOI: https://doi.org/10.1007/bf02017160.

    Collin, Finn. 2011. Science Studies as Naturalized Philosophy. Dordrecht: Springer.

    Collins, Harry M. and Robert Evans. 2002. “The Third Wave of Science Studies: Studies of Expertise and Experience”. Social Studies of Science 32, no. 2: 235-96. https://doi.org/10.1177/0306312702032002003.

    Collison, Robert. 1964. Encyclopaedias: Their History throughout the Ages: A Bibliographical Guide with Extensive Historical Notes to the General Encyclopaedias Issued throughout the World from 350 B.C. to the Present Day. New York: N.Y. Hafner.

    Comte, Auguste. 1830-1842. Cours de philosophie positive 1-6. Paris: Bachelier.

    Cooper, Harris and Larry V. Hedges. 2009. “Research Synthesis as a Scientific Process”. In The Handbook of Research Synthesis and Meta-Analysis. 2nd. ed., eds. Harris Cooper, Harry V. Hedges and Jeffrey C. Valentine. New York: Russel Sage Foundation, 3-16. http://www.webcitation.org/6pwGoeDnv.

    Cooper, Rachel. 2017. “Diagnostic and Statistical Manual of Mental Disorders (DSM)”. Knowledge Organization 44, no. 8: 668-76. Also available in ISKO Encyclopedia of Knowledge Organization, eds. Birger Hjørland and Claudio Gnoli, https://www.isko.org/cyclo/dsm.

    Cope, Bill and Angus Phillips, eds. 2014. The Future of the Academic Journal. 2nd. ed. Oxford: Chandos.

    Crease, Robert P. and Catherine Westfall. 2016. “The New Big Science”. Physics Today 69, no. 5, 30-6. doi: 10.1063/PT.3.3167.

    D’Agostino, Fred. 2015. “Hermeneutics, Epistemology, and Science”. In The Routledge Companion to Hermeneutics, edited by Jeff Malpas and Hans-Helmuth Gander. Milton Park, UK: Routledge, 417-28.

    Danermark, Berth. 2003. “Book review of Nowotny et al. ‘Re-Thinking Science’”. Acta Sociologica 46, no. 2: 166-9.

    Danziger, Kurt. 1990. Constructing the Subject: Historical Origins of Psychological Research. New York: Cambridge University Press.

    Danziger, Kurt. 1997. Naming the Mind: How Psychology Found Its Language. London: Sage.

    Darwin, Charles. 1859. On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. London: J. Murray.

    Daston, Lorraine. 2015. “Science, History of”. In International Encyclopedia of the Social and Behavioral Sciences 2nd. Ed., edited by James D. Wright. Amsterdam: Elsevier, 241-7. http://dx.doi.org/10.1016/B978-0-08-097086-8.62116-5.

    Descartes, René. 1971. Descartes: Philosophical Writings, transl. and ed. Elizabeth Anscombe and Peter Thomas Geach, Indianapolis: Bobbs-Merrill.

    Descartes, René. 1988. Descartes: Selected Writings, trans. John Cottingham, Robert Stoothoff, and Dugald Murdoch, Cambridge: Cambridge University Press.

    Dilthey, Wilhelm. 1894. Ideen über eine beschreibende und zergliedernde Psychologie. Berlin: Verlag der Königlichen Akademie der Wissenschaften.

    Dimitrakos, Thodoris. 2020. “Reconstructing Rational Reconstructions: On Lakatos’s Account on the Relation between History and Philosophy of Science”. European Journal for Philosophy of Science 10, article number: 29. https://doi.org/10.1007/s13194-020-00293-x.

    Dolby, Robert G. A. 1979. “Classification of the Sciences: The Nineteenth Century Tradition”. In Classifications in their Social Contexts, eds. Roy. F. Ellen and David Reason. New York: Academic Press, 167-93.

    Downes, Stephen M. 1997. “Science”. In Encyclopedia of Empiricism, eds. Don Harrett and Edward Barbanell. London, UK: Fitzroy Dearborn, 386-8.

    Downes, Stephen M. 1998. “Constructivism,” In Routledge Encyclopedia of Philosophy, ed. Edward Craig. London: Routledge, Vol. 2: 624-30.

    Droysen, Johann Gustav. [1858], 1937. “Grundriss der Historik”. In Vorlesungen über Enzyklopädie und Methodologie der Geschichte, ed. Rudolf Hübner. München: Oldenbourg, 386-405.

    Duhem, Pierre. 1991. The Aim and Structure of Physical Theory. Translated from La Théorie physique: son objet, sa structure by Philip P. Wiener (after 2nd. Ed. Paris: Marcel Rivière, 1914; 1st. ed. was published in 1906). Princeton: Princeton University Press.

    Dupré, John. 1993. The Disorder of Things: Metaphysical Foundations for the Disunity of Science. Cambridge, MA: Harvard University Press.

    Dupré, John. 2001. “In Defence of Classification”. Studies in History and Philosophy of Biological and Biomedical Sciences 32, no. 2: 203—19.

    Duranti, Luciana and Patricia C. Franks (eds.). 2015. Encyclopedia of Archival Science. Lanham, MD: Rowman & Littlefield.

    Dutch, Steven I. 1982. “Notes on the Nature of Fringe Science”. Journal of Geological Education 30, no. 1: 6—13. doi:10.5408/0022-1368-30.1.6.

    Dutton, William H. and Paul. W. Jeffreys (eds.). 2010. World Wide Research: Reshaping the Sciences and Humanities. Cambridge, MA: MITT Press.

    Edwards, Paul. N. 2010. A Vast Machine. Computer Models, Climate Data, and the Politics of Global Warming. Cambridge, MA: MIT Press.

    Einstein, Albert 1905. “Zur Elektrodynamik bewegter Körper”. Annalen der Physik 17, no. 10: 891—921.

    Einstein, Albert 1949. “Remarks Concerning the Essays Brought Together in this Co-Operative Volume”. In Albert Einstein, Philosopher-Scientist, ed. Paul Arthur Schlipp. New York: Tudor Publishers, 663—88.

    Elkana, Yehuda. 1981. “A Programmatic Attempt at an Anthropology of Knowledge”. In: Sciences and Cultures: Sociology of the Sciences a Yearbook, vol 5, eds. Everett Mendelsohn and Yehuda Elkana. Dordrecht: Springer, 1-76. https://doi.org/10.1007/978-94-009-8429-5_1.

    Encyclopedia Britannica. Chicago, IL: Encyclopædia Britannica, Inc. Online. https://www.britannica.com/ (toll access).

    Erbach, Gregor. 2006. “Data-Centric View in e-Science Information Systems”. Data Science Journal 5: 219-22. DOI: 10.2481/dsj.5.219.

    Ereshefsky, Marc. 2000. The Poverty of the Linnaean Hierarchy: A Philosophical Study of Biological Taxonomy. Cambridge: Cambridge University Press.

    Etzkowitz, Henry. 1998. “The Norms of Entrepreneurial Science: Cognitive Effects of the New University-Industry Linkages”. Research Policy 27, no. 8: 823-33.

    Etzkowitz, Henry and Loet Leydesdorff. 1998. “The Endless Transition: A Triple Helix of University—Industry—Government Relations”. Minerva 36, no. 3: 271—288.

    Feenberg, Andrew. 2017. “A Critical Theory of Technology”. In the Handbook of Science and Technology Studies 4th ed. Eds. Ulrike Felt, Rayvon Fouché, Clark A. Miller and Laurel Smith-Doerr. Cambridge, MA: The MIT Press, 635-63.

    Felt, Ulrike, Rayvon Fouché, Clark A. Miller and Laurel Smith-Doerr (Eds). 2017. The Handbook of Science and Technology Studies. 4th ed. Cambridge, MA: The Mit Press.

    Feyerabend, Paul. 1957. “On the Quantum-Theory of Measurement”. In Observation and Interpretation: A Symposium of Philosophers and Physicists, Proceedings of the Ninth Symposium of the Colston Research Society held in the University of Bristol, April 1st—April 4th, 1957. Ed. Stephan Körner. London: Butterworths, 121—130.

    Fishman, Jennifer R., Lauro Mamo and Patrick R Grzanka. 2017. “Sex, Gender, and Sexuality in Biomedicine”. In the Handbook of Science and Technology Studies 4th ed. Eds. Ulrike Felt, Rayvon Fouché, Clark A. Miller and Laurel Smith-Doerr. Cambridge, MA: The MIT Press, 379-405.

    Fjeldså, Jon. 2013. “Avian Classification in Flux”. In Handbook of the Birds of the World. Special volume 17 Barcelona: Lynx Edicions, 77-146 + references 493-501.

    Fjordback Søndergaard, Trine, Jack Andersen and Birger Hjørland. 2003. “Documents and the Communication of Scientific and Scholarly Information. Revising and Updating the UNISIST Model”. Journal of Documentation 59, no. 3: 278-320.

    Fleck, Ludwig. 1979. Genesis and Development of a Scientific Fact. Translation of Entstehung und Entwicklung einer wissenschaftlichen Tatsache (1935). Chicago: University of Chicago Press.

    Flint, Robert. 1904. Philosophy as Scientia Scientiarum and a History of Classifications of the Sciences. Edinburgh: William Blackwood and Sons.

    Fodor, Jerry A. 1974. “Special Sciences (Or: Disunity of Sciences as a Working Hypothesis)”. Synthese 28, no. 2: 97-115.

    Forman, Paul. 2007. “The Primacy of Science in Modernity, of Technology in Postmodernity, and of Ideology in the History of Technology”. History and Technology 23, no. 1-2: 1-152. https://doi.org/10.1080/07341510601092191.

    Fozooni, Babak. 2012. “The Politics of Encyclopaedias”. Journal for Critical Education Policy Studies 10, no. 2: 314-44. http://www.jceps.com/wp-content/uploads/PDFs/10-2-11.pdf.

    Fraassen, Bas C. van 1980. The Scientific Image. Oxford: Oxford University Press.

    Fraassen, Bas C. van 2002. The Empirical Stance. New Haven: Yale University Press.

    Fraenkel, Carlos, Dario Perinetti and Justin E. H. Smith. Eds. 2011. The Rationalists: Between Tradition and Innovation. The New Synthese Historical Library. Dordrecht: Springer.

    Frické, Martin. 2015. “Big Data and Its Epistemology”. Journal of the Association for Information Science and Technology 66, no. 4: 651-61. DOI: 10.1002/asi.23212

    Frické, Martin. 2019. “The Knowledge Pyramid: the DIKW Hierarchy”. Knowledge Organization 46, no. 1: 33-46. DOI:10.5771/0943-7444-2019-1-33.

    Fuller, Steve. 1998. Science. Minneapolis: University of Minnesota Press.

    Fuller, Steve. 2000. Thomas Kuhn: A Philosophical History for Our Times. Chicago: University of Chicago Press.

    Funtowicz, Silvio O. and Jerome R. Ravetz. 1993. “The Emergence of Post-Normal Science”. In Science, Politics, and Morality: Scientific Uncertainty and Decision Making, edited by René von Schomberg. Dordrecht: Kluwer, 85-123.

    Funtowicz, Silvio O. and Jerome R. Ravetz. 2001. “Post-Normal Science: Science and Governance under Conditions of Complexity”. In Interdisciplinarity in Technology Assessment: Implementation and Its Chances and Limits, edited by Michael Decker, 15-24. Berlin: Springer.

    Furner, Jonathan. 2003a. “Little Book, Big Book: Before and after Little Science, Big Science: A Review Article, Part I”. Journal of Librarianship and Information Science 35, no. 2: 115-25.

    Furner, Jonathan. 2003b. “Little Book, Big Book: Before and after Little Science, Big Science: A Review Article, Part II”. Journal of Librarianship and Information Science 35, no. 3: 189-201.

    Gabbay, Dov M., Paul Thagard and John Woods (eds). 2006-. Handbook of the Philosophy of Science. Amsterdam: Elsevier/North Holland. (Vol. 16 published 2011; no later vol. seems to have appeared.)

    Gabovich, Alexander M. and Vladimir Kuznetsov. 2019. “Towards Periodizations of Science in the History of Science”. In The 15th International History, Philosophy and Science Teaching Conference IHPST 2019 At Aristotle University of Thessaloniki, Greece, July 15.-19. 2019, 584-593. Available at: https://www.researchgate.net/publication/336808966_ Towards_Periodizations_of_Science_in_the_History_of_Science

    Gahegan, Mark. 2020. “Fourth Paradigm GIScience? Prospects for Automated Discovery and Explanation from Data”. International Journal of Geographical Information Science 34, no 1: 1-21, DOI: 10.1080/13658816.2019.1652304.

    Galison, Peter. 2018. “The Pyramid and the Ring: A Physics Indifferent to Ontology”. In Research Objects in their Technological Setting, eds. Bernadette Bensaude Vincent, Sacha Loeve, Alfred Nordmann and Astrid Schwarz. History and Philosophy of Technoscience 10. London: Routledge, 15-26.

    Galison, Peter Louis and David J. Stump. Eds. 1996. The Disunity of Science: Boundaries, Contexts, and Power. Stanford, CA: Stanford University Press.

    Garfield, Eugene. 1979. “Is Citation Analysis a Legitimate Evaluation Tool?” Scientometrics 1, no. 4: 359-75. DOI: https://doi.org/10.1007/BF02019306.

    Garfield, Eugene. 1985. “In Tribute to Derek John de Solla Price: A Citation Analysis of Little Science, Big Science”. Scientometrics 7, nos. 3-6: 487-503.

    Ghiraldelli, Paulo. 2006. “Marxism and Critical Theory”. In A Companion to Pragmatism, eds. John R. Shook and Joseph Margolis. Oxford, UK: Blackwell, 202-8.

    Gibbons, Michael, Camille Limoges, Helga Nowotny, Simon Schwartzman, Peter Scott and Martin Trow. 1994. The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies. London: Sage.

    Giere, Ronald N. 2006. Scientific Perspectivism. Chicago: University of Chicago Press.

    Ginna, Peter. ed. 2017. What Editors Do: The Art, Craft, and Business of Book Editing. Chicago: The University of Chicago Press.

    Ginsparg, Paul. 2009. “Text in a Data-centric World”. In The Fourth Paradigm: Data-intensive Scientific Discovery, eds. Tony Hey, Kristin Michele Tolle and Stewart Tansley. Redmond, VA: Microsoft Research, 185-91. Published OA at: http://research.microsoft.com/en-us/collaboration/fourthparadigm.

    Goble, Carole and David de Roure. 2009. “The Impact of Workflow Tools on Data-Centric Research”. In The Fourth Paradigm: Data-intensive Scientific Discovery, eds. Tony Hey, Kristin Michele Tolle and Stewart Tansley. Redmond, VA: Microsoft Research, 137-145. Published OA at: http://research.microsoft.com/en-us/collaboration/fourthparadigm.

    Goldsmith, Maurice. 1967. “The Science of Science”. Journal of the Royal Society of Arts 115, no. 5131: 518-32. Retrieved from https://www.jstor.org/stable/41371619.

    Golinski, Jan. 1998. Making Natural Knowledge: Constructivism and the History of Science. Cambridge: Cambridge University Press.

    Gray, Jim. 2009. “Jim Gray on eScience: A Transformed Scientific Method”. In The Fourth Paradigm: Data-intensive Scientific Discovery, eds. Tony Hey, Kristin Michele Tolle and Stewart Tansley. Redmond, VA: Microsoft Research, xvii-xxxi. Published OA at: http://research.microsoft.com/en-us/collaboration/fourthparadigm.

    Gutting, Gary. 2000. “Scientific Methodology”. In A Companion to the Philosophy of Science, ed. William H. Newton-Smith. Oxford, UK: Blackwell, 423-32.

    Haack, Susan. 1993a. “A Fallibilist Among the Cynics”. Skeptical Inquirer 23, no. 1: 47-50.

    Haack, Susan. 1993b. “Knowledge and Propaganda. Reflections of an Old Feminist”. Partisan Review 60, no. 4: 556-63. Reprinted in Haack (2009, 331-44).

    Haack, Susan. 1996. “Towards a Sober Sociology of Science”. In The Flight from Science and Reason, eds. Paul R. Gross, Norman Levitt and Martin W. Lewis. New York: Annals of the New York Academy of the Sciences, 259-65.

    Haack, Susan. 2004. “Fallibilism, Objectivity, and the New Cynicism”. Episteme 1, no. 1: 35-48. DOI: 10.3366/epi.2004.1.1.35.

    Haack, Susan. 2009. Evidence and Inquiry: A Pragmatist Reconstruction of Epistemology. Second, expanded edition. Oxford: Blackwell.

    Habermas, Jürgen. 1971. Knowledge and Human Interests. Translated from Erkenntnis und Interesse (1968) and a section of Technik und Wissenschaft als Ideologie (1968) by Jeremy J. Shapiro. Boston: Beacon Press.

    Hacking, Ian. 1999. The Social Construction of What? Cambridge, MA: Harvard University Press.

    Hammersley, Martyn. 2006. “Systematic or Unsystematic, is that the Question? Reflections on the Science, Art, and Politics of Reviewing Research Evidence”. In Public Health Evidence: Tackling Health Inequalities, eds. Amanda Killoran, Catherine Swann and Michael P Kelly. Oxford, UK: Oxford University Press, 239-50.

    Hanegraaff, Wouter. 1996. New Age Religion and Western Culture: Esotericism in the Mirror of Secular Thought. Numen Book Series: Studies in the History of Religions. LXXII. Leiden: Brill Publishers.

    Hanson, Norwood Russell. 1958. Patterns of Discovery: An Inquiry into the Conceptual Foundations of Science. Cambridge, UK: Cambridge University Press.

    Hansson, Sven Ove. 2017. “Science and Pseudo-Science”, Stanford Encyclopedia of Philosophy, ed. Edward N. Zalta. https://plato.stanford.edu/archives/sum2017/entries/pseudo-science/.

    Haraway, Donna. 1997. Modest_Witness@Second_Millennium. FemaleMan_Meets_OncoMouse. New York: Routledge.

    Heelan, Patrick A. 1997. “Why a Hermeneutical Philosophy of the Natural Sciences?” Man and World 30, no. 3: 271-98.

    Heidegger, Martin. 1967. What is a Thing? South Bend, IN: Gateway Editions.

    Hey, Tony, Kristin Michele Tolle and Stewart Tansley. Eds. 2009. The Fourth Paradigm: Data-intensive Scientific Discovery. Redmond, VA: Microsoft Research. Published OA at: http://research.microsoft.com/en-us/collaboration/fourthparadigm.

    Heylighen, Francis, Paul Cilliers and Carlos Gershenson. 2007. “Complexity and Philosophy”. In Complexity, Science and Society, eds. Jan Bogg and Robert Geyer. Oxford: Radcliffe Publishing, 117-34.

    Hjørland, Birger. 1998. “The Classification of Psychology: A Case Study in the Classification of a Knowledge Field”. Knowledge Organization 25, no. 4: 162-201.

    Hjørland, Birger. 2000. “Documents, Memory Institutions, and Information Science”. Journal of Documentation 56, no. 1: 27-41.

    Hjørland, Birger. 2002. “Epistemology and the Socio-Cognitive Perspective in Information Science”. Journal of the American Society for Information Science and Technology 53, no.4: 257-70.

    Hjørland, Birger. 2005. “Empiricism, Rationalism and Positivism in Library and Information Science”. Journal of Documentation 61, no. 1: 130-55. DOI 10.1108/00220410510578050.

    Hjørland, Birger. 2010. “The Foundation of the Concept of Relevance”. Journal of the American Society for Information Science and Technology 61, no. 2: 217-37.

    Hjørland, Birger. 2011a. “The Importance of Theories of Knowledge: Browsing as an Example”. Journal of the American Society for Information Science and Technology 62, no. 3: 594-603. DOI: 10.1002/asi.21480.

    Hjørland, Birger. 2011b. “The Importance of Theories of Knowledge: Indexing and Information Retrieval as an Example”. Journal of the American Society for Information Science and Technology 62, no. 1: 72-7. Doi: 10.1002/asi.21451

    Hjørland, Birger. 2012. “Methods for Evaluating Information Sources: An Annotated Catalogue”. Journal of information science 38, no. 3: 258-68.

    Hjørland, Birger. 2013a. “Facet Analysis: The Logical Approach to Knowledge Organization”. Information processing and management 49, no. 2: 545-57.

    Hjørland, Birger. 2013b. “Theories of Knowledge Organization—Theories of Knowledge”. Knowledge Organization 40, no. 3: 169-81.

    Hjørland, Birger. 2016. “Informetrics Needs a Foundation in the Theory of Science”. In Theories of Informetrics and Scholarly Communication., ed. Cassidy Sugimoto. Berlin: Walter de Gruyter, 20-46.

    Hjørland, Birger. 2018. “Data (with big data and database semantics)”. Knowledge Organization 45, no. 8: 685-708. DOI:10.5771/0943-7444-2018-8-685. Also in ISKO Encyclopedia of Knowledge Organization, eds. Birger Hjørland and Claudio Gnoli. http://www.isko.org/cyclo/data.

    Hjørland, Birger. 2019. “The Foundation of Information Science: One World or Three? A Discussion of Gnoli (2018)”. Journal of Documentation 75, no.1: 164-171. https://doi.org/10.1108/JD-06-2018-0100.

    Hjørland, Birger. 2020. “Political versus Apolitical Epistemologies in Knowledge Organization”. Knowledge Organization 47, no. 6: 461-85. DOI:10.5771/0943-7444-2020-6-461.

    Hjørland, Birger. 2021. “Information Retrieval and Knowledge Organization: A Perspective from the Philosophy of Science”Information 12, no. 3: 135. https://doi.org/10.3390/info12030135.

    Hörnig, Hannes (Johannes). 1985. “Wissenschaft”. In Philosophisches Wörterbuch, edited by Georg Klaus and Manfred Buhr. Westberlin: Das europäische Buch, 2: 1310-3.

    Hookway, Christopher. 2008. “Peirce and Skepticism”. In The Oxford Handbook of Skepticism, ed. John Greco. New York: Oxford University Press, 310-29.

    Hoyningen-Huene, Paul. 2013. Systematicity: The Nature of Science, Oxford: Oxford University Press.

    Hoyningen-Huene, Paul and Simon Lohse. 2015. “Kuhn, Thomas S. (1922-96)”. In International Encyclopedia of the Social & Behavioral Sciences, 2nd Edition, edited by J. D. Wright. Amsterdam: Elsevier, volume 13: 133-38.

    Husserl, Edmund. 2001a. Analyses Concerning Passive and Active Synthesis: Lectures on Transcendental Logic. Dordrecht: Kluwer.

    Husserl, Edmund. 2001b. Logical investigations. Volume 1. London: Routledge. (Originally published in German 1900.)

    Hyland, Ken. 2000. Disciplinary Discourses: Social Interactions in Academic Writing. London, England: Longman.

    Hyman, Ray. 2001. “Parapsychology”. In International Encyclopedia of the Social and Behavioral Sciences, eds. Neil J. Smelser and Paul B. Baltes. Amsterdam: Elsevier, vol. 16: 11031-35.

    Ibekwe-SanJuan, Fidelia and Geoffrey C. Bowker. 2017. “Implications of Big Data for Knowledge Organization”. Knowledge Organization 44, no. 3: 187-98.

    Ingwersen, Peter, Søren Holm, Birger Larsen and Thomas Ploug. 2020. “Do Journals and Corporate Sponsors Back Certain Views in Topics where Disagreement Prevails?” Scientometrics [not yet assigned to an issue]. https://doi.org/10.1007/s11192-020-03743-8.

    Irwin, Alan. 1995. Citizen Science: A Study of People, Expertise and Sustainable Development. London: Routledge.

    Jackson, Mark. Ed. 2011. The Oxford Handbook of the History of Medicine. Oxford, UK: Oxford University Press.

    James, William. 1907. Pragmatism: A New Name for Some Old Ways of Thinking. New York: Longmans Green.

    James, William. 1912. Essays in Radical Empiricism. New York: Longmans, Green, and Co.

    Jeffreys, Paul. W. 2010. “The Developing Conception of e-Research”. In World Wide Research: Reshaping the Sciences and Humanities, eds. William H. Dutton and Paul W. Jeffreys. Cambridge, MA: MITT Press, 51-66.

    Johannessen, Jon-Arild and Johan Olaisen. 2005. ”Systemic Philosophy and the Philosophy of Social Science: Part I: Transcendence of the Naturalistic and the Anti-naturalistic Position in the Philosophy of Social Science”. Kybernetes: The International Journal of Systems & Cybernetics 34, nos. 7/8: 1261-77.

    Johansson, Lars-Göran. 2021. Empiricism and Philosophy of Physics. Cham, Switzerland: Springer.

    Johnson, Ann. 2009. Hitting the Brakes: Engineering Design and the Production of Knowledge. Durham, N.C.: Duke University Press.

    Juul Jensen, Uffe. 1990. “Wissenschaft”. In Europäische Enzyklopädie zu Philosophie und Wissenschaften, hrsg. Hans Jörg Sandkühler. Hamburg: Felix Meiner, vol. 4: 911-21.

    Kellert, Stephen H., Helen E. Longino and C. Kenneth Waters (eds). 2006. Scientific Pluralism. Minneapolis, MN: University of Minnesota Press.

    Kemp, Stephen. 2005. “Saving the Strong Programme? A Critique of David Bloor’s Recent Work“. Studies in History and Philosophy of Science 36, no. 4: 706-19. doi: 10.1016/j.shpsa.2005.08.002.

    Kennedy, Mary Lee. 2018. “The Opportunity for Research Libraries in 2018 and Beyond”. Portal-Libraries and the Academy 18, no. 4: 629-37.

    Khalidi, Muhammad Ali. 2013. Natural Categories and Human Kinds: Classification in the Natural and Social Sciences. Cambridge, UK: Cambridge University Press.

    Kim, Jaegwon and Ernest Sosa (eds). 1995. A Companion to Metaphysics. Oxford: Blackwell.

    Kincaid, Harold. 1998. “Positivism in the Social Sciences”. In Routledge Encyclopedia of Philosophy ed. Edward Craig. London: Routledge, vol. 7: 558-61.

    Klee, Robert. 1997. Introduction to Philosophy of Science: Cutting Nature by Its Seams. New York: Oxford University Press.

    Kockaert, Hendrik J. and Frieda Steurs. Eds. 2015. Handbook of Terminology, Vol. 1. Amsterdam: John Benjamins. DOI: https://doi.org/10.1075/hot.1.

    König, Jason, and Greg Woolf. 2013. Encyclopaedism from Antiquity to the Renaissance. Cambridge: Cambridge University Press.

    Koertge, Noretta. 2000. “Science, Values, and the Value of Science”. Philosophy of Science 67, Supplement, Proceedings of the 1998 Biennial Meetings of the Philosophy of Science Association. Part II: Symposia Papers: S45-S57. Stable URL: https://www.jstor.org/stable/188657.

    Kronick, David A. 1962. A History of Scientific and Technical Periodicals. The Origins and Development of the Scientific and Technological Press, 1665-1790. New York: Scarecrow Press.

    Krummel, Donald William. 2017. “Bibliography”. In Encyclopedia of Library and Information Sciences, Fourth Edition, eds. John D. McDonald and Michael Levine-Clark. Boca Raton, FL: CRC Press, 468-79. DOI: 10.1081/E-ELIS4-120044335.

    Kuhn, Thomas S. 1962. The Structure of Scientific Revolutions. Chicago, IL: University of Chicago Press.

    Kuhn, Thomas S. 1970. “Reflections on My Critics”. In Criticism and the Growth of Knowledge eds. Imre Lakatos and Alan Musgrave. Cambridge, MA: Cambridge University Press, 231-78.

    Kuhn, Thomas S. 2000. The Road Since Structure: Philosophical Essays, 1970-1993, with an Autobiographical Interview, eds. John Haugeland and James F. Conant. Chicago: University of Chicago Press.

    Kuipers, Theo A. F. 2001. Structures in Science, Heuristic Patterns Based on Cognitive Structures: An Advanced Textbook in Neo-Classical Philosophy of Science. Dordrecht: Kluwer.

    Lakatos, Imre. 1976. Proofs and Refutations: The Logic of Mathematical Discovery. Eds. John Worrall and Elie Zahar. Cambridge, UK: Cambridge University Press.

    Lakatos, Imre. 1978. The Methodology of Scientific Research Programmes: Philosophical Papers vol. 1, eds. John Worrall and Gregory Currie. Cambridge: Cambridge University Press.

    Langmuir, Irving, and Robert N. Hall. 1989. “Pathological Science”. Physics Today 42, no. 10: 36-48.

    Latour, Bruno. 1987. Science in Action: How to Follow Scientists and Engineers Through Society. Cambridge, MA: Harvard University Press.

    Latour, Bruno. 1988. The Pasteurization of France. Cambridge, MA: Harvard University Press.

    Latour, Bruno. 1993. We Have Never Been Modern. Cambridge, MA: Harvard University Press.

    Latour, Bruno. 1999. “For David Bloor… and Beyond: A Reply to David Bloor’s ‘Anti-Latour’”. Studies in History and Philosophy of Science 30, no. 1: 113-29.

    Latour, Bruno. 2004. “Why Has Critique Run out of Steam? From Matters of Fact to Matters of Concern”. Critical Inquiry 30, no. 2: 225-48. https://doi.org/10.1086/421123.

    Latour, Bruno. 2014. “Agency in the Time of the Anthropocene”. New Literary History 45, no. 1: 1-18.

    Latour, Bruno and Steve Woolgar. 1979. Laboratory Life. The Social Construction of Scientific Facts. Princeton, N.J.: Princeton University Press.

    Latour, Bruno and Steve Woolgar. 1986. Laboratory Life. The Construction of Scientific Facts. Princeton, N.J.: Princeton University Press.

    Laudan, Larry. 1983. “The Demise of the Demarcation Problem”. In Physics, Philosophy and Psychoanalysis: Essays in Honor of Adolf Grünbaum, eds. Robert S. Cohen and Larry Laudan. Boston Studies in the Philosophy of Science 76. Dordrecht, Holland: Springer, 111-27. DOI: 10.1007/978-94-009-7055-7_6.

    Laudan, Larry, 1984. “The Pseudo-Science of Science?” in Scientific Rationality: The Sociological Turn, ed. James Brown. Dordrecht: D. Reidel, 41-74.

    Law, John. 2009. “Actor Network Theory and Material Semiotics”. In The New Blackwell Companion to Social Theory, ed. Bryan S. Turner. Chichester, UK: Wiley-Blackwell, 141-58.

    Lee, Carole J., Cassidy R. Sugimoto, Guo Zhang and Blaise Cronin. 2013. “Bias in Peer Review”. Journal of the American Society for Information Science and Technology 64, no. 1: 2-17. DOI:10.1002/asi.22784

    Legg, Catherine and Hookway, Christopher. 2019. “Pragmatism”. In The Stanford Encyclopedia of Philosophy (Spring 2019 Edition), ed. Edward N. Zalta. https://plato.stanford.edu/archives/spr2019/entries/pragmatism/.

    Lennox, James. 1997. “Darwin, Charles”. In Don Garrett and Edward Barbanell (eds) Encyclopedia of Empiricism. London: Fitzroy Dearborn Publishers, 78-80.

    Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago: The University of Chicago Press.

    Levi, Isaac. 2006. “Inquiry, Deliberation, and Method”. In A Companion to Pragmatism, eds. John R. Shook and Joseph Margolis. Malden, MA: Blackwell, 378-85.

    Levins, Richard and Richard Lewontin. 2009. The Dialectical Biologist. Delhi, India: Aakar.

    Lindberg, David C. and Ronald L. Numbers Eds. 2002-2020. The Cambridge History of Science Vol. 1-8. New York: Cambridge University Press.

    Lindsey, Duncan. 1978. The Scientific Publication System in Social Science: A Study of the Operation of Leading Professional Journals in Psychology, Sociology and Social Work. San Francisco: Jossey-Bass Publishers.

    Lock, Stephen P. 1989. “‘Journalogy’: Are the Quotes Needed?” CBE Views 12, no. 4: 57-59. Reprinted in Current Contents, 1990, #3, 21-24. (With an introduction by Eugene Garfield, 19-21). Available from: http://www.garfield.library.upenn.edu/essays/v13p019y1990.pdf.

    Loux, Michael J. and Dean W. Zimmerman (eds.). 2005. The Oxford Handbook of Metaphysics. Oxford: Oxford University Press. DOI: 10.1093/oxfordhb/9780199284221.001.0001.

    Lugg, Andrew. 1987. “Bunkum, Flim-Flam and Quackery: Pseudoscience as a Philosophical Problem”. Dialectica 41: 221-30.

    Lynch, Clifford A. 2003. “Institutional Repositories: Essential Infrastructure for Scholarship in The Digital Age”. Portal-Libraries and the Academy 3, no. 2: 327-36. DOI: 10.1353/pla.2003.0039.

    Lynch, Clifford A. 2009. “Jim Gray’s Fourth Paradigm and the Construction of the Scientific Record “. In The Fourth Paradigm: Data-intensive Scientific Discovery, eds. Tony Hey, Kristin Michele Tolle and Stewart Tansley. Redmond, VA: Microsoft Research, 177-83. Published OA at: http://research.microsoft.com/en-us/collaboration/fourthparadigm.

    Mahner, Martin. 2007. “Demarcating Science from Non-Science”. In General Philosophy of Science: Focal Issues, ed. Theo Kuipers. Handbook of the Philosophy of Science, eds. Dov M. Gabbay, Paul Thagard and John Woods. Amsterdam: Elsevier, 515-75.

    Mahner, Martin. 2013. “Science and Pseudoscience: How to Demarcate after the (Alleged) Demise of the Demarcation Problem”. In Philosophy of Pseudoscience: Reconsidering the Demarcation Problem, eds. Massimo Pigliucci and Maarten Boudry. Chicago: University of Chicago Press, 29-43.

    Mallery, John C., Roger Hurwitz, and Gavan Duffy. 1992. “Hermeneutics“. In: Encyclopedia of Artificial Intelligence. Vol. 1-2. 2nd. ed. Ed. by Stuart C. Shapiro. New York: John Wiley & Sons, Vol 1, 596-611.

    Margolis, Joseph. 2009. Culture and Cultural Entities: Towards a New Unity of Science. 2nd. Ed. New York: Springer.

    Marx, Karl and Friedrich Engels. 1964. Marx-Engels-Werke Bd. 25., Das Kapital, Bd. 3. Berlin: Institut für Geschichte der Arbeiterbewegung.

    Mazzocchi, Fulvio. 2015. “Could Big Data be the End of Theory in Science? A Few Remarks on the Epistemology of Data-Driven Science”. EMBO Reports 16, no. 10: 1250-55. DOI 10.15252/embr.201541001

    McAllister, James. W. 2000. “Relativism”. In A Companion to the Philosophy of Science, ed. William H. Newton-Smith. Oxford, UK: Blackwell, 405-7.

    Merton, Robert King. 1938. “Science, Society, and Technology in Seventeenth Century England”. In OSIRIS: Studies on the History and Philosophy of Science and on the History of Learning and Culture, Volume IV, Part 2. Ed. George Sarton. Bruges, Belgium: The St Catherine Press: 362-632.

    Merton, Robert King. 1942. “The Normative Structure of Science. In The Sociology of Science: Theoretical and Empirical Investigations by Robert King Merton (1973). Chicago: University of Chicago Press.

    Merton, Robert King. 1963. “Resistance to the Systematic Study of Multiple Discoveries in Science”. European Journal of Sociology 4, no. 2: 237-82.

    Merton, Robert King. 1968. Social Theory and Social Structure, enlarged edition. New York: Free Press.

    Merton, Robert King. 1977. “The Sociology of Science: An Episodic Memoir”. In The Sociology of Science in Europe, eds. Robert K. Merton and Jerry Gaston. Carbondale, Illinois: Southern Illinois University Press, 3-141.

    Merton, Robert King. 1996. On Social Structure and Science, edited and with an introduction by Piotr Sztompka. Chicago: University of Chicago Press.

    Merton, Robert K. and Elinor Barber. 2004. The Travels and Adventures of Serendipity: A Study in Sociological Semantics and the Sociology of Science. Princeton, N.J.: Princeton University Press.

    Midtgarden, Torjus. 2020. “Peirce’s Classification of the Sciences”. Knowledge Organization 47, no. 3: 267-78. Also in ISKO Encyclopedia of Knowledge Organization, eds. Birger Hjørland and Claudio Gnoli, https://www.isko.org/cyclo/peirce.

    Miedema, Frank. 2012. Science 3.0: Real Science, Real Knowledge. Amsterdam: Amsterdam University Press.

    Miksa, Francis L. 1998. The DDC, The Universe of Knowledge, and the Post-Modern Library. Albany, New York: Forest Press.

    Mirowski, Philip. 2017. “What is Science Critique? Lessig, Latour”. In The Routledge Handbook of the Political Economy of Science, eds. David Tyfield, Rebecca Lave, Samuel Randalls and Charles Thorpe. London, UK: Routledge, 429-50.

    Moed, Henk F. 2005. Citation Analysis in Research Evaluation. Information Science and Knowledge Management Volume 9. Dordrecht, Netherlands: Springer.

    Morin, Edgar. 2008. On Complexity. Cresskill, NJ: Hampton Press. (Translated by Robin Postel from La Complexité humaine.)

    Morris, Sally, Ed Barnas, Douglas LaFrenier and Margaret Reich. 2013. The Handbook of Journal Publishing. Cambridge: Cambridge University Press.

    Muniesa, Fabian. 2015. “Actor-Network Theory”. In International Encyclopedia of the Social & Behavioral Sciences, 2nd edition, ed. James D Wright. Amsterdam: Elsevier vol. 1: 80-4. https://doi.org/10.1016/B978-0-08-097086-8.85001-1.

    Musgrave, Alan and Charles Pigden. 2016. “Imre Lakatos”. In the Stanford Encyclopedia of Philosophy (Winter 2016 Edition), ed. Edward N. Zalta. https://plato.stanford.edu/archives/win2016/entries/lakatos/.

    Nelson, Alan Jean. Ed. 2005. A Companion to Rationalism. Blackwell Companions to Philosophy. Oxford, UK: Blackwell Publishing.

    Newton, Isaac. 1687. Philosophiæ Naturalis Principia Mathematica. London: Societatis Refiae ac Typis Josephi Streater.

    Newton-Smith, William H. 2000. A Companion to the Philosophy of Science. Oxford, UK: Blackwell.

    Nickles, Thomas. 2005. “Empiricism”. In New Dictionary of the History of Ideas ed. Horowitz Maryanne Cline. New York: Charles Scribner's Sons, vol. 2: 664-9.

    Nickles, Thomas. 2017. “Historicist Theories of Scientific Rationality”. In The Stanford Encyclopedia of Philosophy, ed. Edward N. Zalta. https://plato.stanford.edu/archives/sum2017/entries/rationality-historicist/.

    Nicolaisen, Jeppe. 2007. “Citation Analysis”. Annual Review of Information Science and Technology 41, 609-41.

    Nordmann, Alfred, Hans Radder and Gregor Schiemann. Eds. 2011. Science Transformed? Debating Claims of an Epochal Break. Pittsburgh, PA: University of Pittsburgh Press.

    Novick, Peter. 1988. That Noble Dream: The “Objectivity Question” and the American Historical Profession. Cambridge UK: Cambridge University Press.

    Norvig, Peter. 2008. “All We Want are the Facts, Ma’am”. Retrieved from http://norvig.com/fact-check.html.

    Nowotny, Helga, Peter Scott, and Michael Gibbons. 2001. Re-thinking Science: Knowledge and the Public in an Age of Uncertainty. Cambridge, UK: Polity.

    Nunberg, Geoffrey (ed.). 1996. The Future of the Book. With an afterword by Umberto Eco. Berkeley, CA: University of California Press.

    Olby, Robert C., Geoffrey N. Cantor, John R.R. Christie and M. Jonathon S. Hodge eds. 1990. Companion to the History of Modern Science. London: Routledge

    Omodeo, Pietro Daniel. 2019. Political Epistemology: The Problem of Ideology in Science Studies. Charm, Switzerland: Springer Nature.

    Oppenheim, Paul and Hilary Putnam. 1958. “Unity of Science as a Working Hypothesis”, in Minnesota Studies in the Philosophy of Science, Volume II: Concepts, Theories, and the Mind-Body Problem, ed. Herbert Feigl, Michael Scriven and Grover Maxwell. Minneapolis, MN: University of Minnesota Press, 3-36.

    Ossenblok, Truyken. 2016. Scientific Communication in the Social Sciences and Humanities: Analysis of Publication and Collaboration Patterns in Flanders. PhD-dissertation. Antwerp, Belgium: University of Antwerp.

    Oxford English Dictionary: The Definite Record of the English Language (OED). Accessed January 6, 2021 from https://www.oed.com/ (Paywall).

    Panofsky, Wolfgang K. H. 1992. “SLAC [the Stanford Linear Accelerator Center] and Big Science: Stanford University”. In Big Science: The Growth of Large-Scale Research, eds. Peter Galison and Bruce Hevly. Stanford, CA.: Stanford University Press, 129-48.

    Peirce, Charles Sanders. 1878. “How to Make Our Ideas Clear”. Popular Science Monthly 12, January: 286-302.

    Peirce, Charles Sanders. 1902. “On Science and Natural Classes”. In The Essential Peirce, Selected Philosophical Writings, Vol. 2: 1893-1913. Eds. Nathan Houser and Christian J. W. Kloesel. Bloomington, IN: Indiana University Press, 1992: 115-32.

    Peirce, Charles Sanders. 1903. “Pragmatism as the Logic of Abduction”. Harvard Lectures on Pragmatism VII. Published in The Essential Peirce, Selected Philosophical Writings, Vol. 2: 1893-1913. Eds. Nathan Houser and Christian J. W. Kloesel. Bloomington, IN: Indiana University Press, 1992: 226-241.

    Peirce, Charles Sanders. 1905. “What Pragmatism Is”. The Monist 15, no. 2: 161-81.

    Petrovich, Eugenio. 2020. “Science mapping”. In ISKO Encyclopedia of Knowledge Organization, eds. Birger Hjørland and Claudio Gnoli, https://www.isko.org/cyclo/science_mapping; also in press in Knowledge Organization.

    Pigliucci, Massimo. 2013. “The Demarcation Problem: A (Belated) Response to Laudan”. In Philosophy of Pseudoscience: Reconsidering the Demarcation Problem, eds. Massimo Pigliucci and Maarten Boudry. Chicago: University of Chicago Press, 9-28.

    Pihlström, Sami. 2014. “Pragmatic Realism”. In Realism, Science, and Pragmatism, ed. Kenneth R. Westphal. New York: Routledge, 251-282.

    Pihlström, Sami. 2017. “Pragmatic Realism, Idealism, and Pluralism: A Rescherian Balance?” In Pragmatism and Objectivity: Essays Sparked by the Work of Nicholas Rescher, ed. Sami Pihlström. New York: Routledge, 7-30. https://helda.helsinki.fi//bitstream/handle/10138/310740/ Pihlstr_m_Practical_Realism.pdf?sequence=1

    Poincare, Henri. 1905. Science and Hypotheses. New York: The Walter Scott Publishing Co. Available at https://www.gutenberg.org/files/37157/37157-pdf.pdf.

    Poli, Roberto and Johanna Seibt (eds.). 2010. Theory and Applications of Ontology: Philosophical Perspectives. Dordrecht: Springer.

    Poli, Roberto, Michael Healy and Achilles Kameas (eds.). 2010. Theory and Applications of Ontology: Computer Applications. Dordrecht: Springer.

    Popper, Karl R. 1957. The Poverty of Historicism. London: Routledge.

    Popper, Karl R. 1959. The Logic of Scientific Discovery. (Translated by the author from the German Logik der Forschung: Zur Erkenntnistheorie der modernen Naturwissenschaft from 1934). London: Hutchinson.

    Popper, Karl R. 1963. Conjectures and Refutations: The Growth of Scientific Knowledge. London: Routledge & Kegan Paul.

    Poser, Hans. 2001. Wissenschaftstheorie: Eine philosophische Einführung. Stuttgart: Reclaim.

    Price, Derek John de Solla. 1963. Little Science, Big Science. New York: Columbia University Press.

    Price, Derek John de Solla. 1970. “Citation Measures of Hard Science, Soft Science, Technology, and Nonscience”. In Communication Among Scientists and Engineers, eds. Donald K Pollock and Carnot E Nelson. Lexington, MA: Heath Lexington Books, 3-22.

    Putnam, Hilary. 1994. “Pragmatism and Moral Objectivity”. In Words and Life, ed. James F. Conant. Cambridge MA: Harvard University Press, 151-81.

    Quine, Willard Van Orman. 1969. Ontological Relativity and Other Essays. New York: Columbia University Press.

    Radnitzky, Gerald. 1970. Contemporary Schools of Metascience: Anglosaxon Schools of Metascience, Continental Schools of Metascience. 2nd. Ed. Göteborg: Akademiförlaget.

    Ransom, Elizabeth, Maki Hatanaka, Jason Konefal and Allison Loconto. 2017. “Science and Standards”. In The Routledge Handbook of the Political Economy of Science, eds. David Tyfield, Rebecca Lave, Samuel Randalls and Charles Thorpe. London, UK: Routledge, 329-40.

    Ravetz, Jerome R. and Silvio O Funtowicz. 2015. “Science, New Forms of”. In International Encyclopedia of the Social & Behavioral Sciences 2nd. edition, ed. James D Wright. Amsterdam: Elsevier, vol. 21: 248-54. http://dx.doi.org/10.1016/B978-0-08-097086-8.85027-8.

    Remington, John A. 1988. “Beyond Big Science in America: The Binding of Inquiry”. Social Studies of Science 18, no. 1: 45-72.

    Rescher, Nicholas. 2006. “Pragmatic Idealism and Metaphysical Realism”. In A companion to Pragmatism, eds. John R. Shook and Joseph Margolis. Oxford, UK: Blackwell, 386-97.

    Richards, Richard A. 2016. Biological Classification: A Philosophical Introduction. Cambridge, UK: Cambridge University Press.

    Richardson, Ernest E. 1930. Classification: Theoretical and Practical. 3rd. ed. New York: H.W. Wilson.

    Rip, Arie. 2002. “Science for the Twenty-First Century”. In The Future of the Sciences and Humanities: Four Analytical Essays and a Critical Debate on the Future of Scholastic Endeavour, edited by Peter Tindemans, Alexander Verrijn-Stuart, and Rob Visser. Amsterdam: Amsterdam University Press, 99-148.

    Rodriguez-Navarro, Alonso. 2009. “Sound Research, Unimportant Discoveries: Research, Universities, and Formal Evaluation of Research in Spain”. Journal of the American Society for Information Science and Technology 60, no. 9: 1845-58.

    [Roos, Anna Marie]. 2018. “Life Histories, or History Comes to Life” (editorial). Notes and Records: The Royal Society Journal of the History of Science 72, no. 3: 195-8. doi:10.1098/rsnr.2018.0044.

    Ross, George MacDonald. 1990. “Science and Philosophy”. In Companion to the History of Modern Science, Eds. Robert C. Olby, Geoffrey N. Cantor, John R.R. Christie and M. Jonathon S. Hodge. London: Routledge, 799-815.

    Rouse, Joseph. 2005. “Heidegger on Science and Naturalism”. In Continental Philosophy of Science, ed. Gary Gutting. Malden: Blackwell, 121-41.

    Rydenfelt, Henrik. 2014. “Scientific Method and the Realist Hypothesis”. In Charles Sanders Peirce in His Own Words: 100 Years of Semiotics, Communication and Cognition, eds. Torkild Thellefsen and Bent Sørensen. Berlin: De Gruyter Mouton.

    Sadegh-Zadeh, Kazem. 2015. “On the Concept of Science”, Handbook of the Analytic Philosophy of Medicine. Second Edition. Dordrecht, Netherlands: Springer, vol. 2: 856-65.

    Samurin, Evgenii Ivanovich. 1964. Geschichte der bibliotekarisch-bibliographischen Klassifikation. Band I-II. Leipzig: VEB Bibliographisches Institut.

    Sandoz, Raphaël. 2018. Interactive Historical Atlas of the Disciplines. Geneva: University of Geneva. Online source available at: https://atlas-disciplines.unige.ch/.

    Sarvimäki, Anneli. 1988. Knowledge in Interactive Practice Disciplines: An Analysis of Knowledge in Education and Health Care. Research Bulletin No. 68. Helsinki, Finland: University of Helsinki, Department of Education. Available at: https://files.eric.ed.gov/fulltext/ED305329.pdf.

    Schäfer, Wolf, ed. 1983. Finalization in Science: The Social Orientation of Scientific Progress. Dordrecht: Reidel.

    Schmidt, Jan C. 2007. Instabilität in Natur und Wissenschaft: Eine Wissenschaftsphilosophie der nachmodernen Physik. Berlin: de Gruyter.

    Schöpfel, Joachim and Dominic J. Farace. 2010a. “Grey Literature”. Encyclopedia of Library and Information Sciences, Third Edition, eds. Marcia J. Bates and Mary Niles Maack. Boca Raton, FL: CRC Press, vol. 3: 2029-39.

    Schöpfel, Joachim and Dominic J. Farace. Eds. 2010b. Grey Literature in Library and Information Studies. Berlin: De Gruyter Saur. DOI: https://doi.org/10.1515/9783598441493. Freely available at: https://www.degruyter.com/view/title/34553.

    Schöpfel, Joachim, Dominic Farace, Hélène Prost, Antonella Zane and Birger Hjørland. 2020. “Data Documents”. ISKO Encyclopedia of Knowledge Organization, eds. Birger Hjørland and Claudio Gnoli. Available at: https://www.isko.org/cyclo/data_documents.

    Schuster, John Andrew. 1995a. An Introduction to the History and Social Studies of Science. Wollongong, New South Wales, Australia: Dept. of Science & Technology Studies, University of Wollongong. Available at: https://descartes-agonistes.com/2020/03/07/ introduction-to-the-history-and-social-studies-of-science/

    Schuster, John Andrew. 1995b. The Scientific Revolution: An Introduction to the History and Philosophy of Science. Wollongong, New South Wales, Australia: Dept. of Science & Technology Studies, University of Wollongong. Available at: https://descartes-agonistes.com/2020/03/07/scientific-revolution-intro-to-hps/.

    Schuster, John Andrew and Richard R. Yeo. 1986. “Introduction”. In The Politics and Rhetoric of Scientific Method: Historical Studies, eds. John Andrew Schuster and Richard R. Yeo. Dordrecht, Netherland: D. Reidel, ix-xxxvii.

    Scientometrics: An International Journal for all Quantitative Aspects of the Science of Science, Communication in Science and Science Policy. Published monthly by Akadémiai Kiadó, Budapest and Springer, Dordrecht, 1978-.

    Shaw, Ryan. 2020. “Periodization”. In ISKO Encyclopedia of Knowledge Organization, eds. Birger Hjørland and Claudio Gnoli. https://www.isko.org/cyclo/periodization (also in press in Knowledge Organization).

    Silvertown, Jonathan. 2009. “A New Dawn for Citizen Science”. Trends in Ecology & Evolution 24, no. 9: 467-71. DOI: https://doi.org/10.1016/j.tree.2009.03.017.

    Slezak, Peter. 1994. “A Second Look at David Bloor’s ‘Knowledge and Social Imagery’. Philosophy of the Social Sciences 24, no. 3: 336-61. https://doi.org/10.1177/004839319402400304.

    Slife, Brent D. and Nathan M. Slife. 2014. “Empiricism, Essay”. In Encyclopedia of Critical Psychology, ed. Thomas Teo. New York: Springer, 571-8. DOI 10.1007/978-1-4614-5583-7.

    Slife, Brent D. and Richard N. Williams. 1995. What's Behind the Research? Discovering Hidden Assumptions in the Behavioral Sciences. London: SAGE Publications.

    Slota, Stephen C and Geoffrey C. Bowker. 2017. “How Infrastructures Matter”. In the Handbook of Science and Technology Studies 4th ed. Eds. Ulrike Felt, Rayvon Fouché, Clark A. Miller and Laurel Smith-Doerr. Cambridge, MA: The MIT Press, 529-54.

    Sluys, Ronald, Koen Martens and Frederick R. Schram. 2004. “The PhyloCode: Naming of Biodiversity at a Crossroads”. Trends in Ecology and Evolution 19, no. 6: 280-1. DOI: 10.1016/j.tree.2004.04.001cal.

    Small, Henry. 2016. “Referencing as Cooperation or Competition”. In Theories of Informetrics and Scholarly Communication: A Festschrift in Honor of Blaise Cronin, ed. Cassidy Sugimoto. Berlin: Walter de Gruyter, 49-71.

    Smutny, Zdenek and Vasja Vehovar. 2020. “Social Informatics Research: Schools of Thought, Methodological Basis, and Thematic Conceptualization”. Journal of the Association for Information Science and Technology 71, no. 5: 529-39. DOI: 10.1002/asi.

    Socientize Consortium. 2013. Green Paper on Citizen Science, Citizen Science for Europe: Towards a Better Society of Empowered Citizens and Enhanced Research. The Socientize Consortium of the European Commission. https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=4122.

    Sokal, Alan D. and Jean Bricmont. 1998. Fashionable Nonsense: Postmodern Intellectuals' Abuse of Science. Translated from Impostures intellectuelles (Paris: Editions Odile Jacob, 1997). New York: Picador.

    Sokal, Robert R. and Peter H. A. Sneath. 1963. Principles of Numeric Taxonomy. San Francisco, CA: W.H. Freeman.

    Sosa, Ernest. 1998. “Foundationalism”. In Routledge Encyclopedia of Philosophy, edited by Edward Craig. London: Routledge doi: 10.4324/9780415249126-P021-1

    Spang-Hanssen, Henning. 2001. “How to teach about information as related to documentation”. Human IT 5, no. 1: 125-143. http://etjanst.hb.se/bhs/ith/1-01/hsh.htm.

    Suchting, Wal A. 1997. “Reflections on Peter Slezak and the Sociology of Scientific Knowledge”. Science & Education 6, nos. 1-2: 151-95. DOI: https://doi.org/10.1023/A:1008622114675.

    Swales, John M. 2004. Research Genres: Explorations and Applications. Cambridge: Cambridge University Press.

    Sztompka, Piotr. 1986. Robert K. Merton: An Intellectual Profile. London: Macmillan.

    Teller, Paul. 2019. “What Is Perspectivism, and Does It Count as Realism?”. In Understanding Perspectivism: Scientific Challenges and Methodological Prospects, eds. Michela Massimi and Casey D. McCoy. New York, NY: Routledge, 49-64. Freely available at: https://doi.org/10.4324/9781315145198.

    Thagard, Paul. 1992. Conceptual Revolutions. Princeton, NJ: Princeton University Press.

    Thagard, Paul. 2012. The Cognitive Science of Science: Explanation, Discovery, and Conceptual Change. Cambridge, MA: The MIT Press.

    Thelwall, Mike. 2019. “The Rhetorical Structure of Science? A Multidisciplinary Analysis of Article Headings”. Journal of Informetrics 13, no. 2: 555-63.

    Thelwall, Mike. In Press. “Measuring Societal Impacts of Research with Altmetrics? Common Problems and Mistakes”. Journal of Economic Surveys. doi: 10.1111/joes.12381; Freely available at https://onlinelibrary-wiley-com.ep.fjernadgang.kb.dk/doi/pdfdirect/10.1111/joes.12381.

    Toulmin, Stephen. 1963. Foresight and Understanding: An Enquiry into the Aims of Science. New York, NY: Harper.

    Toulmin, Stephen. 1992. Cosmopolis: The Hidden Agenda of Modernity. Chicago: University of Chicago Press.

    Tsou, Jonathan Y. 2015. “Reconsidering the Carnap-Kuhn Connection”. In Kuhn’s Structure of Scientific Revolutions—50 Years On, eds. William J. Devlin and Alisa Bokulich. Boston Studies in the Philosophy and History of Science 311. Cham, Switzerland: Springer, 51-69. DOI 10.1007/978-3-319-13383-6_5.

    UNISIST. 1971. Study Report on the Feasibility of a World Science Information System, by the United Nations Educational, Scientific and Cultural Organization and the International Council of Scientific Unions. Paris: UNESCO.

    Vahrenkamp, Richard. 2017. The First Informationexplosion: The Role of Punch Card Technology in the Office Rationalization in Germany, 1910-1939. Working Paper on the History of Computing No. 1/2017. Berlin: Logistik Consulting. Retrieved from: https://www.researchgate.net/publication/339069204_The_First_Informationexplosion _The_Role_of_Punch_Card_Technology_in_the_Office_Rationalization_in_Germany.

    Vergo, Peter. Ed. 1989. The New Museology. London: Reaktion Books.

    Vernon, René E. 2020. “The Location and Composition of Group 3 of the Periodic Table”. Foundations of Chemistry (not yet assigned to an issue). DOI: https://doi.org/10.1007/s10698-020-09384-2.

    Vogel, Steven. 2017. “What is the “Philosophy of Praxis”?”. In Critical Theory and the Thought of Andrew Feenberg, eds. Darrell P. Arnold and Andreas Michel. Cham, Switzerland: Palgrave Macmillan/Springer, 17-45. DOI 10.1007/978-3-319-57897-2_2.

    Watson, John B. 1913. “Psychology as the Behaviorist Views It”. Psychological Review 20, no. 2: 158-77.

    Web of Science. Philadelphia, PA: Clarivate Analytics. https://clarivate.com/webofsciencegroup/solutions/web-of-science/ (Toll access).

    Weinberg, Alvin M. 1961. “Impact of Large-Scale Science on the United States — but We Have Yet to Make the Hard Financial and Educational Choices it Imposes”. Science 134, no. 347: 161-4. Accessed December 5, 2020. http://www.jstor.org/stable/1708292.

    Westfall, Catherine. 2003. “Rethinking Big Science: Modest, Mezzo, Grand Science and the Development of the Bevalac, 1971-1993”. Isis 94, no. 1: 30-56. doi: 10.1086/376098.

    Westphal, Kenneth R. Ed. 2017. Realism, Science, and Pragmatism. Routledge Studies in Contemporary Philosophy. London: Routledge.

    Whewell, William. 1834. “On the Connexion of the Physical Sciences. By Mrs. Somerville”. Quarterly Review 51, no. 102: 54-68.

    White, Michael J. 2017. “Patents and Patent Searching”. In Encyclopedia of Library and Information Sciences, Fourth Edition, eds. John D. McDonald and Michael Levine-Clark. Boca Raton, FL: CRC Press, 3560-72. DOI: 10.1081/E-ELIS4-120043653

    Wilbanks, John. 2009. “I Have Seen the Paradigm Shift, and It Is Us”. In The Fourth Paradigm: Data-intensive Scientific Discovery, eds. Tony Hey, Kristin Michele Tolle and Stewart Tansley. Redmond, VA: Microsoft Research, 209-14. Published OA at: http://research.microsoft.com/en-us/collaboration/fourthparadigm.

    Wilkins, John S. and Malte C. Ebach. 2014. The Nature of Classification: Relationships and Kinds in the Natural Sciences. New York: Palgrave MacMillan.

    Wittich, Dieter. 1985. “Praxis”. In Philosophisches Wörterbuch, edited by Georg Klaus and Manfred Buhr. Westberlin: Das europäische Buch, 2: 964-71.

    Yeates, Stuart. 2017. “After Beall's "List of Predatory Publishers": Problems with the List and Paths Forward”. Information Research 22, no. 4, paper rails1611. Retrieved from http://InformationR.net/ir/22-4/rails/rails1611.html.

    Zalabardo, José L. 2019. “The Primacy of Practice”. Royal Institute of Philosophy Supplement 86: 181-99. doi:10.1017/S1358246119000122.

    Zammito, John. 2007. “What’s ‘New’ in the Sociology of Knowledge?” In Handbook of the Philosophy of Science: Philosophy of Anthropology and Sociology, eds. Stephen P. Turner and Mark W. Risjord. Amsterdam: Elsevier, 791-857. https://doi.org/10.1016/B978-044451542-1/50025-8.

    Ziman, John M. 1996a. “Is Science Losing Its Objectivity?” Nature 382, no. 6594: 751-4.

    Ziman, John M. 1996b. “’Post-Academic Science’: Constructing Knowledge with Networks and Norms”. Science Studies 9, no. 1: 67-80. https://sciencetechnologystudies.journal.fi/article/view/55095/17930.

    Ziman, John M. 1998. “Basically, It's Purely Academic”. Interdisciplinary Science Reviews, 23:2, 161-8, DOI: 10.1179/isr.1998.23.2.161.

    Ziman, John M. 2000. Real Science: What It Is, and What It Means. Cambridge: Cambridge University Press.

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    Appendix 1: A Marxist Understanding of Science

    From Hörnig 1985; translated from German; emphasis in original. [This version is a reprint of the version printed in the 12. edition, 10. Print run (1976); originally based on an anonymous article in the 1964 edition of Philosophisches Wörterbuch; a former version authored by Hörnig appeared in the 1972 edition.]


    “Science”: - complex social appearance and an essential part of the social reproduction process.

    The emergence and development of science ultimately depends on the productive confrontation of people with their external nature; it is the result of the growing power of its practical trading. All major innovations in science have their starting point in practice, especially in material production, and find their realization in practice. Science penetrates ever deeper into the laws of nature, social development and thought, enables foresight and transformation of reality in the interests of society. Its knowledge is the basis of human activity and enables the growing mastery of the natural and social environment.

    Science is generally characterized by the process of developing knowledge and the historically developed system of scientific knowledge and the individual sciences. Both sides are closely related and mutually dependent. Science is primarily a social process; its development is largely determined by the respective social formation.

    The development and appropriation of knowledge is a creative and social work process. Scientific work, every discovery and invention, is general work. “It is partly due to cooperation with the living, partly through the use of earlier work” (Marx / Engels [1964] 25, 114 ). Science is above all scientific activity. Knowledge is a prerequisite and the result of scientific activity. This point of view corresponds to a dialectical-materialistic view and becomes more and more important in the practice of the socialist social order, in the design of scientific and technical progress. Knowledge becomes science, not only from the point of view of registering facts etc., but rather from the point of view of continuing, deepening basic knowledge and also scientific working methods.

    From the point of view of its results, science is a historically developed system of knowledge about nature, society and thought, which has objective (relative) truth. This system of knowledge is fixed in concepts, statements (especially scientific laws), theories and hypotheses. Science is the highest generalization of practice and, in an abstract form, conveys correct knowledge of the nature of phenomena, of the laws of nature and society.

    The structure of science reflects the real process of understanding, the interrelations between theory and practice. Science includes empirical knowledge of facts that have been developed through experience, observation and experiment. The most essential element of science is theoretical knowledge. It is created by arranging known appearances, connections and understanding, through abstraction and systematization. The emergence of a theory is primarily characterized by the development of a system of fact about the objective Regularities of an object. For example, it was only with the work of Euclid that mathematics was given a systematic and demonstrative character. The elementary knowledge of chemical processes did not reach the level of a science before Boyle et al. provided the necessary knowledge that enabled chemistry gradually to be transformed to a science. It was only with this and with the development of the necessary concepts that many facts could be explained.

    Political economy and social sciences could only develop into science with the epoch-making discoveries of Marx and Engels about the economic social formations.

    When new facts become known, which cannot be explained with the current state of knowledge, new theories arise, and new areas of knowledge develop.

    Hypotheses and scientific predictions (forecasts) must also be included in the area of theoretical knowledge.

    All theories are checked and corrected in practice and are incorporated into the theoretical system of science as confirmed scientific knowledge.

    Of particular importance to science are its ideological and philosophical foundations and conclusions. All scientific theories are highly related to philosophical questions. This affects all areas of science. Both the individual sciences and the system of sciences as a whole cannot do without ideological and philosophical prerequisites and problems.

    Theses on the liberation of science from philosophy and ideology, as represented by bourgeois ideologists, especially those of a positivist nature, only document the dilemma of bourgeois ideology in explaining the nature of science and confirm its unscientific character.

    As a scientific worldview and philosophy, dialectical and historical materialism represents the ideological, epistemological, and methodological basis of all sciences.

    Scientific knowledge is embedded in the overall social work process. Science can only become a productive force in this context. Originally indirectly included in the practical production activity, science has increasingly become a special area of the social division of labor as a result of the division of physical and intellectual work. This area of social work includes a large and growing number of scientific institutions, scientific workers, and scientific organizations.

    This process of socializing science has also made it an important institution that requires enormous material resources to support it. Especially in the field of natural science, a broad industrial-technical basis is today required.

    The ever-evolving division of scientific labor requires scientific management, planning and extensive organizational work. It must be designed in accordance with the objective requirements of scientific and technical progress and the management of science increasingly has the character of scientific work.

    The rapid progress of human knowledge has led to a very extensive differentiation of the fields of knowledge and the emergence of numerous new special disciplines. This further specialization of science is a necessary process and expression for the fact that man, with his knowledge, penetrates deeper and deeper into laws and thus acquires an ever-greater ability to master the laws of nature and society. Since the individual sciences are concerned with the exploration of certain sides of objective reality or their reflection in consciousness, their development must at the same time bring the different branches of science closer together. There is also an inner connection between the sciences, there are mutual relationships that have a general meaning. The differentiation also includes the ever-stronger integration of the fields of knowledge. The scientific activities therefore include the interrelations and interactions of different fields of knowledge.

    Of great importance for this integration are such sciences as Marxist-Leninist philosophy, mathematics, and cybernetics, which examine relationships and laws that are common to qualitatively different objects.

    The differentiation and integration of the sciences are two sides of a real dialectical process of ever deeper knowledge of objective reality and better mastery of the processes in nature and society. The successful application of scientific knowledge in practice depends to a large extent on a correct understanding of the dialectic of scientific and technical progress, the place and the possibilities of a special science as well as its methods and relationships with other sciences.

    Science is particularly important as an educational factor. In this context, it occupies a relatively independent place in the social reproductive process by elaborating and imparting the necessary knowledge bases and the methods of acquiring knowledge for all forms of education. In socialism, science and scientific education are the foundations of the education system of the whole population. The socialist state enables every working person to acquire science through all-round and continuous education.

    This development of the all-round and continuous education of the working people is fundamentally conditioned and made possible by the requirements of scientific, technical, and social progress, the increasingly intensive integration of science and production and the development of socialist democracy.

    The interrelation between science and production is becoming increasingly important due to the scientific and technical revolution. A closer interaction between science and production is an essential prerequisite for rapid development and for changes in almost all areas of society. The results of science increasingly influence the technical level of production, while production at the level of scientific and technical progress increases the possibilities of science.

    Despite the constant increase in the role of science for the level of production and the performance of social processes, science remains an inseparable and dependent part of social practice. The progress of science is ultimately determined by social practice, primarily by the development of production.

    The management, planning and organization of science is aimed at high growth to solve the main task of socialist society and to increase the effectiveness of scientific and technical work. Science is increasingly becoming an immediate productive force (German: Produktivkraft).

    Such a direct relationship between science and production does not mean narrowing the tasks, the structure or the different levels of science. It is only possible through a deeper penetration into the laws of nature and society, the development of new production systems and technologies in production preparation, the scientific management of production and a conscious design of social relationships. For this purpose, both extensive basic research and extensive applied research are necessary. The necessary scientific advance for the production of tomorrow, the development of forecasts as well as the possibility of planned scientific and technical progress in socialism increase the importance of the subjective factor of society.

    The increasing socialization of science and the acceleration of scientific and technical progress require a new quality of scientific information and documentation, which to a certain extent maintains the character of scientific knowledge.

    The importance of science for society also results from its history. It is part of the history of human society, the struggle of the classes for historical progress. Science is essentially human and revolutionary. Scientists’ constant struggle for new knowledge, better mastery of the laws of nature and society and improving people's lives. It is incompatible with stagnation and reaction.

    Science can only do justice to its revolutionary and human nature if it is based on the most revolutionary class in society. Great insights, which above all initiate a qualitative development in the history of science, were always borne and promoted by the class, which itself was interested in the changes in existing social conditions, while the conservative classes and their ideologists oppose the revolutionary science. The ideological struggle took a dominant place in these debates. The fundamental achievements of Copernicus, Kepler, and Galilei in the field of astronomy, physics and mechanics, the development of the experiment as scientific methodology and practical demonstration of evidence, for example, trembled the foundations of the feudal society and its world view and withdrawn from the representatives of the clergy their claim of the supremacy of divine Authority. That is why the ruling forces went against science and its most revolutionary representatives by all means of political and economic coercion and did not shy away from murder. Until today, reactionary philosophy has been characterized by skepticism in answering science's claim to objective truth, and by replacing the close relationship between science and worldview with an idealistic worldview and subjectivism. This attitude also corresponds to the one-sided and extremely pragmatic relationship of the monopoly bourgeoisie and its state to science. It expresses the limits of capitalist society for the development of science.

    The imperialist bourgeoisie tries to use the achievements of science to increase profits and maintain their class rule. Science has today become an important factor in competition between capitalist countries.

    The history of science makes it clear that scientific knowledge can only practice its usefulness for mankind if it meets the needs of historical progress and is supported by the most progressive forces. The ruling bourgeois class and its ideologists reject scientific knowledge that justifies social progress as an objective lawfulness and the unity of natural and social science because it makes clear the demise of capitalist society and the communist future. The history of science teaches that it is science's responsibility to fight against reaction and anti-human theories.

    In the 20th century, social progress was directed towards communism; it is connected with the scientific and technical revolution, with the all-round development of science and is based on the discoveries of the laws of nature, society and thought and its basis of unity and universality.

    Science lives up to its essence and responsibility for humanity if it is based on the most revolutionary class of the 20th century, the working class, and is guided by its scientific worldview, Marxism-Leninism.

    The most important driving force for the development of science to the immediate productive force in the present is the socialist relations of production. The degree of mastery of the natural and social environment develops not only quantitatively but also qualitatively in socialism, because science in the politics of the party of the working class also becomes the basis for the development of society. The primary task of science is to strengthen socialism”.

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    Version 1.0 published 2021-04-22

    Article category: Core concepts in KO

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