Skip to main content
SpringerLink
Log in

Towards the discovery of scientific revolutions in scientometric data

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Paradigms and revolutions are popular concepts in science studies and beyond, yet their meaning is notoriously vague and their existence is widely disputed. Drawing on recent developments in agent-based modeling and scientometric data, this paper offers a precise conceptualization of paradigms and their dynamics, as well as a number of hypotheses that could in principle be used to test for the existence of scientific revolutions in scientometric data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Kuhn had appointed James Conant and the late John Haugeland to finish the manuscript (Nickles 2003). Even 20 years after Kuhn’s death the book is still “forthcoming” according to James Conant and Chicago University Press.

  2. On page 178 Kuhn (1970) references an early book by his friend Garfield (1964) who would come to be known as one of the founders of scientometrics.

  3. The book sold more than 1 million copies making it one of the most widely distributed philosophical books in the world.

  4. “Bibliometric techniques could be used to determine how long a research problem (“puzzle”) has gone unsolved and gauge the number of researchers working on it, to yield a measure of the difficulty of puzzles” (Sterman and Wittenberg 1999).

  5. “From a more theoretical point of view, an interesting goal for future work is to understand the origin of the universality found and how its precise functional form comes about” (Radicchi et al. 2008).

  6. “The existence of a general theory and detailed model that describes the formation of scientific fields across disciplines, time, and population size would provide a new comprehensive, quantitative, and predictive framework with which to understand the social and conceptual dynamics that drive the self-organized creation of scientific communities. Such a framework would be of significant interest to scientists and would hold great promise for guiding science policy” (Bettencourt and Kaiser 2015).

  7. According to Kuhn there is “a feedback loop through which theory change affects the values which led to that change” (Kuhn 1977, 336).

  8. “Sketching the needed reconceptualization, I’ve indicated three of its main aspects. First, that what scientists produce, and evaluate is not belief tout court but change of belief, a process which I’ve argued has intrinsic elements of circularity, but of a circularity that is not vicious. Second, that what evaluation aims to select is not beliefs that correspond to a so-called real external world, but simply the better or best of the bodies of belief actually present to the evaluators at the time their judgments are reached. [...] And, finally, I’ve suggested that the plausibility of this view depends upon abandoning the view of science as a single, monolithic enterprise, bound by a unique method. Rather, it should be seen as a complex but unsystematic structure of distinct specialties or species, each responsible for a different domain of phenomena” (Kuhn 2000, 119).

  9. For general reviews of the literature on the meaning of citations, see Bornmann and Daniel (2008) and Leydesdorff (1998).

  10. For a different approach to observing scientific revolutions in scientometric data, see Marx and Bornmann (2013).

  11. For another application of Uzzi et al. (2013), Lee et al. (2015) use its method to test the effects of team size and variety on creativity.

  12. For an analysis of novelty and impact in teams, see Lee et al. (2015).

  13. See also Langhe and Rubbens (2015) for additional analysis of this model.

  14. “This central role of an elaborate and often esoteric tradition is what I have principally had in mind when speaking of the essential tension in scientific research. I do not doubt that the scientist must be, at least potentially, an innovator, that he must possess mental flexibility, and that he must be prepared to recognize troubles where they exist. That much of the popular stereotype is surely correct [...] But what is no part of our stereotype and what appears to need careful integration with it is the other face of this same coin. We are, I think, more likely fully to exploit our potential scientific talent if we recognize the extent to which the basic scientist must also be a firm traditionalist” (Kuhn 1977, 239, my italics).

  15. Kuhn illustrates the benefits of specialization for the electricians using the Franklinian paradigm: “Freed from the concern with any and all electrical phenomena, the united group of electricians could pursue selected phenomena in far more detail, designing much special equipment for the task and employing it more stubbornly and systematically than electricians had ever done before. Both fact collection and theory articulation became highly directed activities. The effectiveness and efficiency of electrical research increased accordingly” (Kuhn 1970, 18).

  16. See for example Popper (1970), Scheffler (1982) and Shapere (1984).

  17. The model was written using the Netlogo software package version 4.1.3.

References

  • Andersen, H., Barker, P., & Chen, X. (2006). The cognitive structure of scientific revolutions. Abingdon: Routledge.

    Book  Google Scholar 

  • Arthur, B. (1989). Competing technologies, increasing returns, and lock-in by historical events. Economic Journal, 394, 116–131.

    Article  Google Scholar 

  • Arthur, B. (1994). Increasing returns and path-dependence in the economy. Ann Arbor: University of Michigan Press.

    Book  Google Scholar 

  • Bettencourt, L., & Kaur, J. (2011). Evolution and structure of sustainability science. PNAS, 108(49), 19,540–19,545.

    Article  Google Scholar 

  • Bettencourt, L., Kaiser, D., & Kaur, J. (2009). Scientific discovery and topological transitions in collaboration networks. Journal of Informetrics, 3, 210–221.

    Article  Google Scholar 

  • Bettencourt, M., & Kaiser, D. (2015). Formation of scientific fields as a universal topological transition. SFI Working Paper (2015-03-009). http://www.santafe.edu/media/workingpapers/15-03-009.pdf.

  • Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(3), 7–7280.

    Google Scholar 

  • Bornmann, L., & Daniel, H. D. (2008). What do citation counts measure? Journal of Documentation, 64(1), 45–80.

    Article  Google Scholar 

  • Chen, C. (2012). Turning points. Berlin: Springer.

    Book  Google Scholar 

  • Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3(3), 191–209.

    Article  Google Scholar 

  • De Langhe, R. (2013). The Kuhnian paradigm. Topoi, 32(1), 65–73.

    Article  Google Scholar 

  • De Langhe, R., & Greiff, M. (2010). Standards and the distribution of cognitive labour. Logic Journal of the IGPL, 18(2), 278–294.

    Article  MathSciNet  Google Scholar 

  • De Langhe, R., & Rubbens, P. (2015). From theory choice to theory search: the essential tension between exploration and exploitation in science. In W. Devlin & A. Bokulich (Eds.), Kuhn’s structure of scientific revolutions—50 years on (pp. 105–114). Berlin: Springer.

    Google Scholar 

  • Fuller, S. (2001). Thomas Kuhn: A philosophical history for our times. Chicago: Chicago University Press.

    Google Scholar 

  • Garfield, E. (1964). The use of citation data in writing the history of science. Philadelphia: Institute of Scientific Information.

    Google Scholar 

  • Gigerenzer, G. (2000). Simple heuristics that make us smart. Oxford: Oxford University Press.

    Google Scholar 

  • Holland, J. (1998). Emergence from chaos to order. Oxford: Oxford University Press.

    MATH  Google Scholar 

  • Hoyningen-Huene, P. (1993). Reconstructing scientific revolutions. Chicago: Chicago University Press.

    Google Scholar 

  • Kuhn, T. (1970). The structure of scientific revolutions (2nd ed.). Chicago: Chicago University Press. ([1962]).

    Google Scholar 

  • Kuhn, T. (1977a). The essential tension. Chicago: Chicago University Press.

    Google Scholar 

  • Kuhn, T. (1977b). The essential tension: Tradition and innovation in scientific research. The essential tension (pp. 225–239). Chicago: Chicago University Press. ([1959]).

    Google Scholar 

  • Kuhn, T. (2000). The road since structure: Philosophical essays 1970–1993. Chicago: Chicago University Press.

    Google Scholar 

  • Lee, Y. N., Walsh, J., & Wang, J. (2015). Creativity in scientific teams: Unpacking novelty and impact. Research Policy, 44(3), 684–697.

    Article  Google Scholar 

  • Leydesdorff, L. (1998). Theories of citation? Scientometrics, 43(1), 5–25.

    Article  Google Scholar 

  • March, J. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.

    Article  Google Scholar 

  • Marx, W., & Bornmann, L. (2013). The emergence of plate tectonics and the Kuhnian model of paradigm shift: A bibliometric case study based on the anna karenina principle. Scientometrics, 94(2), 595–614.

    Article  Google Scholar 

  • Masterman, M. (1970). The nature of a paradigm. In I. Lakatos & A. Musgrave (Eds.), Criticism and the growth of knowledge (pp. 59–89). Chicago: Chicago University Press.

    Chapter  Google Scholar 

  • Mazloumian, A., Eom, Y. H., Helbing, D., Lozano, S., & Fortunato, S. (2011). How citation boosts promote scientific paradigm shifts and nobel prizes. PLoS One, 6(5), e18975.

    Article  Google Scholar 

  • Miller, J., & Page, S. (2007). Complex adaptive systems. Princeton: Princeton University Press.

    MATH  Google Scholar 

  • Moravcsik, M., & Murugesan, P. (1979). Citation patterns in scientific revolutions. Scientometrics, 1(2), 161–169.

    Article  Google Scholar 

  • Nickles, T. (2003). Thomas Kuhn. Cambridge: Cambridge University Press.

    Google Scholar 

  • Popper, K. (1970). Normal science and its dangers. In I. Lakatos & A. Musgrave (Eds.), Criticism and the growth of knowledge (pp. 8–51). Chicago: Chicago University Press.

    Google Scholar 

  • Radicchi, F., Fortunato, S., & Castellano, C. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. PNAS, 105(45), 17,268–17,272.

    Article  Google Scholar 

  • Railsback, S., & Grimm, V. (2011). Agent-based and individual-based modeling. Princeton: Princeton University Press.

    MATH  Google Scholar 

  • Scheffler, I. (1982). Science and subjectivity. New York: Hackett Publishing Company.

    Google Scholar 

  • Shapere, D. (1984). Meaning and scientific change. Boston Studies in the Philosophy of Science, 78, 58–101.

    Article  Google Scholar 

  • Sharrock, W., & Read, R. (2003). Does thomas kuhn have a ’model of science’? Social Epistemology, 17(2–3), 6–293.

    Google Scholar 

  • Small, H. (2003). Paradigms, citations, and maps of science: A personal history. Journal of the American Society for Information Science and Technology, 54(5), 394–399.

    Article  MathSciNet  Google Scholar 

  • Sterman, J. (1985). The growth of knowledge: Testing a theory of scientific revolutions with a formal model. Technological forecasting and social change. Technological Forecasting and Social Change, 28(2), 93–122.

    Article  Google Scholar 

  • Sterman, J., & Wittenberg, J. (1999). Path dependence, competition, and succession in the dynamics of scientific revolution. Organization Science, 10(3), 322–341.

    Article  Google Scholar 

  • Sun, X., Kaur, J., Milojevic, S., Flammini, A., & Menczer, F. (2013). Social dynamics of science. Scientific Reports, 3, 1069.

    Google Scholar 

  • Toulmin, S. (1970). Does the distinction between normal and revolutionary science hold water? In I. Lakatos & A. Musgrave (Eds.), Criticism and the growth of knowledge (pp. 39–47). Chicago: Chicago University Press.

    Chapter  Google Scholar 

  • Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. (2013). Atypical combinations and scientific impact. Science, 342, 468–472.

    Article  Google Scholar 

  • Wray, B. (2011). Kuhn’s evolutionary social epistemology. Cambridge: Cambridge University Press.

    Book  Google Scholar 

Download references

Acknowledgments

The author wishes to thank Peter Rubbens, Jonathan Leliaert, and Benjamin Vandermarliere for their comments and support; Eric Schliesser and Erik Weber of the Centre for Logic and Philosophy of Science, and Koen Schoors and Jan Ryckebusch of the Complex Systems Institute at Ghent University for their support and encouragement; and the Research Foundation - Flanders (FWO) for supporting this project.

Author information

Authors and Affiliations

  1. Centre for Logic and Philosophy of Science, Ghent University, Blandijnberg 2, 9000, Ghent, Belgium

    Rogier De Langhe

Authors
  1. Rogier De Langhe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rogier De Langhe.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De Langhe, R. Towards the discovery of scientific revolutions in scientometric data. Scientometrics 110, 505–519 (2017). https://doi.org/10.1007/s11192-016-2108-x

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-016-2108-x

Keywords

Search

Navigation