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Interdisciplinarity and insularity in the diffusion of knowledge: an analysis of disciplinary boundaries between philosophy of science and the sciences

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Abstract

Two fundamentally different perspectives on knowledge diffusion dominate debates about academic disciplines. On the one hand, critics of disciplinary research and education have argued that disciplines are isolated silos, within which specialists pursue inward-looking and increasingly narrow research agendas. On the other hand, critics of the silo argument have demonstrated that researchers constantly import and export ideas across disciplinary boundaries. These perspectives have different implications for how knowledge diffuses, how intellectuals gain and lose status within their disciplines, and how intellectual reputations evolve within and across disciplines. We argue that highly general claims about the nature of disciplinary boundaries are counterproductive, and that research on the nature of specific disciplinary boundaries is more useful. To that end, this paper uses a novel publication and citation network dataset and statistical models of citation networks to test hypotheses about the boundaries between philosophy of science and 11 disciplinary clusters. Specifically, we test hypotheses about whether engaging with and being cited by scientific communities outside philosophy of science has an impact on one’s position within philosophy of science. Our results suggest that philosophers of science produce interdisciplinary scholarship, but they tend not to cite work by other philosophers when it is published in journals outside of their discipline. Furthermore, net of other factors, receiving citations from other disciplines has no meaningful impact—positive or negative—on citations within philosophy of science. We conclude by considering this evidence for simultaneous interdisciplinarity and insularity in terms of scientific trading theory and other work on disciplinary boundaries and communication.

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Notes

  1. One legitimate reason for excluding the humanities is that available bibliometric data has less coverage. Books are more important in the humanities than in the social sciences, and books are not well indexed.

  2. More specifically, we use Exponential Random Graph Models (ERGMs), which are relatively new development in network analysis (Lusher et al. 2012; Robins et al. 2007). ERGMs are rare in the sociology of science and information science, but researchers are starting to use them in bibliometric (Fanelli and Glänzel 2013) and knowledge management research (Jiang et al. 2015; Lungeanu et al. 2014; Su and Contractor 2011; Škerlavaj et al. 2010).

  3. Philosophy of Science; British Journal for the Philosophy of Science; Studies in History and Philosophy of Science, Parts A, B, and C; Synthese; European Journal for Philosophy of Science; Journal for General Philosophy of Science; and International Studies in the Philosophy of Science. We arrived at this list by checking journal rankings and corresponding with several highly-regarded senior philosophers of science over email.

  4. Unfortunately, we did not have access to the membership lists for other philosophy of science associations.

  5. We had a team of 61 research assistants disambiguate authors manually. Author disambiguation is a major challenge in bibliometric research. While our approach is not perfect, current automated methods of author disambiguation are much more likely to have high rates of false positive or false negatives, depending on the approach adopted.

  6. The subject classifications are widely used in bibliometric research, especially when it comes to research on interdisciplinary citations and knowledge diffusion. However, they have been criticized for being somewhat arbitrary. We grouped the subject classifications into broader disciplinary clusters because (1) these more general clusters require fewer arbitrary classification decisions, and (2) having fewer categories greatly reduces the complexity of our statistical network models.

  7. Readers familiar with generalized linear models but unfamiliar with exponential random graph models may wish to consult the detailed comparison in Lusher et al. (2012).

  8. Initially, this seems to contradict the well-established Matthew effect (Merton 1973)—cumulative advantage—in academic citations. However, the gwidegree term improves the goodness of fit of the model. We suspect that this is because, for most philosophy articles, the probability of being highly cited is fairly low. In other words, gwidegree helps us control for the fact the most philosophy of science articles are not highly cited. Of course, bigger fields with higher publication and citation rates may differ.

  9. The top-ranked journal variable indicates whether or not the article was published in a journal whose impact factor is within the top 10% within its disciplinary context. The seven core philosophy of science journals are not identified based on their impact factors. Instead, they are the journals that philosophers of science have indicated are the major specialized journals within their field.

  10. By including these terms, we ensure that significant two-path and clustering effects are separate from the functional dependencies that arise from edges, popularity spread, and activity spread.

  11. Of course, we do not mean to suggest that this is the only reason for citation clustering. There are complex reasons for why authors cite some authors and not others. However, when looking at patterns within and across intellectual fields, decisions about which author or paper to cite are most likely to come into play within some smaller subset of authors and articles, not the entire field. For example, if I am networks researcher making a decision about who to cite for an overview of statistical approaches to network analysis, I am more likely to cite someone who is actually doing work on statistical models for networks than someone working in another area of network science. Within the subset of authors working on statistical network models, I may make citation decisions in ways that favour people I know, or who have cited me, etc. Given the type of models and analysis in this paper, we think it is reasonable to assume that the citation clustering we see if primarily driven by specialization, even if individual citation decisions between one author or another are shaped by other considerations.

  12. For readers unfamiliar with ERGMs, in the two goodness of fit plots in this paper, goodness of fit should considered good if the thick black lines (observed statistics) are aligned with the simulations (the boxplots).

  13. The positive effect for this term may be due to several non-exclusive mechanisms. Papers in major journals may be more visible, thus receiving greater attention. Authors may choose to cite papers from major journals on the premise that they provide stronger evidence than papers from less prestigious journals. The major journals may get the highest quality submissions from the field, allowing them to pick the best, which are then subsequently recognized by other philosophers in the form of citations.

References

  • Bettencourt, L., Kaiser, D., Kaur, J., Castillo-Chavez, C., & Wojick, D. (2008). Population modeling of the emergence and development of scientific fields. Scientometrics, 75(3), 495–518.

    Article  Google Scholar 

  • Börner, K., Chen, C., & Boyack, K. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179–255.

    Article  Google Scholar 

  • Börner, K., Klavans, R., Patek, M., Zoss, A., Biberstine, J., Light, R., et al. (2012). Design and update of a classification system: The UCSD map of science. PLoS ONE, 7(7), e39464.

    Article  Google Scholar 

  • Boyack, K., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351–374.

    Article  Google Scholar 

  • Campbell, D. (1969). Ethnocentrism of disciplines and the fish-scale model of omniscience. Interdisciplinary relationships in the social sciences, 328, 348.

    Google Scholar 

  • Chen, S., Arsenault, C., Gingras, Y., & Larivière, V. (2014). Exploring the interdisciplinary evolution of a discipline: The case of biochemistry and molecular biology. Scientometrics, 102(2), 1307–1323.

    Article  Google Scholar 

  • Chen, S., Arsenault, C., & Larivière, V. (2015). Are top-cited papers more interdisciplinary? Journal of Informetrics, 9(4), 1034–1046.

    Article  Google Scholar 

  • Chen, C., & Hicks, D. (2004). Tracing knowledge diffusion. Scientometrics, 59(2), 199–211.

    Article  Google Scholar 

  • Coccia, M., & Bozeman, B. (2016). Allometric models to measure and analyze the evolution of international research collaboration. Scientometrics, 108(3), 1065–1084.

    Article  Google Scholar 

  • Coccia, M., & Wang, L. (2016). Evolution and convergence of the patterns of international scientific collaboration. Proceedings of the National Academy of Sciences, 113(8), 2057–2061.

    Article  Google Scholar 

  • Collins, R. (2009). The sociology of philosophies. Cambridge: Harvard University Press.

    Google Scholar 

  • Collins, H. (2011). Language and practice. Social Studies of Science, 41(2), 271–300.

    Article  Google Scholar 

  • Collins, H., Evans, R., & Gorman, M. (2010). Trading zones and interactional expertise. In M. Gorman (Ed.), Trading zones and interactional expertise (pp. 7–25). Cambridge: MIT Press.

    Chapter  Google Scholar 

  • Crane, D. (1972). Invisible colleges: Diffusion of knowledge in scientific communities. Chicago: University of Chicago Press.

    Google Scholar 

  • Cronin, B., & Meho, L. I. (2008). The shifting balance of intellectual trade in information studies. Journal of the American Society for Information Science and Technology, 59(4), 551–564.

    Article  Google Scholar 

  • Cronin, B., & Pearson, S. (1990). The export of ideas from information science. Journal of information science, 16(6), 381–391.

    Article  Google Scholar 

  • Cronin, B., & Sugimoto, C. (2014). Beyond bibliometrics: Harnessing multidimensional indicators of scholarly impact. Cambridge: MIT Press.

    Google Scholar 

  • Fanelli, D., & Glänzel, W. (2013). Bibliometric evidence for a hierarchy of the sciences. PLoS ONE, 8(6), e66938.

    Article  Google Scholar 

  • Fehr, C., & Plaisance, K. S. (2010). Socially relevant philosophy of science: An introduction. Synthese, 177(3), 301–316.

    Article  Google Scholar 

  • Frodeman, R. (Ed.). (2010). The Oxford handbook of interdisciplinarity. Oxford: Oxford University Press.

    Google Scholar 

  • Galison, P. (1997). Image and logic: A material culture of microphysics. Chicago: University of Chicago Press.

    Google Scholar 

  • Gao, X., & Guan, J. (2011). Network model of knowledge diffusion. Scientometrics, 90(3), 749–762.

    Article  Google Scholar 

  • Gorman, M. (2002). Levels of expertise and trading zones: A framework for multidisciplinary collaboration. Social Studies of Science, 32(5–6), 933–938.

    Google Scholar 

  • Gorman, M. (2010). Trading zones and interactional expertise: Creating new kinds of collaboration. Cambridge: MIT Press.

    Book  Google Scholar 

  • Gross, N. (2002). Becoming a pragmatist philosopher: Status, self-concept, and intellectual choice. American Sociological Review, 67(1), 52–76.

    Article  Google Scholar 

  • Gross, N. (2009). Richard Rorty: The making of an American philosopher. Chicago: University of Chicago Press.

    Google Scholar 

  • Handcock, M., Hunter, D., Butts, C., Goodreau, S., & Morris, M. (2008). Statnet: Software tools for the representation, visualization, analysis and simulation of network data. Journal of Statistical Software, 24(1), 1548.

    Article  Google Scholar 

  • Herrera, M., Roberts, D. C., & Gulbahce, Natali. (2010). Mapping the evolution of scientific fields. PLoS ONE, 5(5), e10355.

    Article  Google Scholar 

  • Hunter, D., Goodreau, S., & Handcock, M. (2008). Goodness of fit of social network models. Journal of the American Statistical Association, 103(481), 248–258.

    Article  MathSciNet  MATH  Google Scholar 

  • Hunter, D., Goodreau, S., & Handcock, M. (2013). Ergm. Userterms: A template package for extending statnet. Journal of Statistical Software, 52(2), i02.

    Article  Google Scholar 

  • Ilhan, A. (2013). The growth of the design disciplines in the united states 1984–2010. Ph.D. thesis. Washington State University.

  • Jacobs, J. (2014). In defence of disciplines: Interdisciplinarity and specialization in the research university. Chicago: Chicago University Press.

    Book  Google Scholar 

  • Jacobs, J., & Frickel, S. (2009). Interdisciplinarity: A critical assessment. Annual Review of Sociology, 35, 43–65.

    Article  Google Scholar 

  • Jiang, S., Gao, Q., Chen, H., & Roco, M. (2015). The roles of sharing, transfer, and public funding in nanotechnology knowledge-diffusion networks. Journal of the Association for Information Science and Technology, 66(5), 1017–1029.

    Article  Google Scholar 

  • Klein, J. T. (1990). Interdisciplinarity: History, theory, and practice. Detroit: Wayne State University Press.

    Google Scholar 

  • Koskinen, J., & Snidjers, T. (2013). Simulation, estimation, and goodness of fit. eds. Dean Lusher, Koskinen Johan, and Garry Robins.: 141–66.

  • Kreuzman, H. (2001). A co-citation analysis of representative authors in philosophy: Examining the relationship between epistemologists and philosophers of science. Scientometrics, 50(3), 525–539.

    Article  Google Scholar 

  • Lamont, M. (2009). How professors think: Inside the curious world of academic judgment. Cambridge: Harvard University Press.

    Book  Google Scholar 

  • Larivière, V., & Gingras, Y. (2014). Measuring interdisciplinarity. In B. Cronin & C. Sugimoto (Eds.), Beyond bibliometrics: Harnessing multidimensional indicators of scholarly impact (pp. 187–200). Cambridge: MIT.

    Google Scholar 

  • Larivière, V., Sugimoto, C. R., & Cronin, B. (2012). A bibliometric chronicling of library and information science’s first hundred years. Journal of the Association for Information Science and Technology, 63(5), 997–1016.

    Google Scholar 

  • Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the Isi subject categories. Journal of the American Society for Information Science and Technology, 60(2), 348–362.

    Article  Google Scholar 

  • Light, R., & Adams, J. (2016). Knowledge in motion: The evolution of HIV/Aids research. Scientometrics, 107(3), 1227–1248.

    Article  Google Scholar 

  • Liu, X., Jiang, S., Chen, H., Larson, C. A., & Roco, M. C. (2015). Modeling knowledge diffusion in scientific innovation networks: An institutional comparison between China and US with illustration for nanotechnology. Scientometrics, 105(3), 1953–1984.

    Article  Google Scholar 

  • Lockett, A., & McWilliams, A. (2005). The balance of trade between disciplines: Do we effectively manage knowledge? Journal of Management Inquiry, 14(2), 139–150.

    Article  Google Scholar 

  • Lungeanu, A., Huang, Y., & Contractor, N. (2014). Understanding the assembly of interdisciplinary teams and its impact on performance. Journal of Informetrics, 8(1), 59–70.

    Article  Google Scholar 

  • Lusher, D., Koskinen, J., & Robins, G. (2012). Exponential random graph models for social networks: Theory, methods, and applications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • McLevey, J., & McIlroy-Young, R. (2017). Introducing Metaknowledge: Software for computational research in information science, network analysis, and science of science. Journal of Informetrics, 11(1), 176–197.

    Article  Google Scholar 

  • Merton, R. (1973). The sociology of science: Theoretical and empirical investigations. Chicago: University of Chicago Press.

    Google Scholar 

  • Moody, J., & Light, R. (2006). A view from above: The evolving sociological landscape. The American Sociologist, 37(2), 67–86.

    Article  Google Scholar 

  • Panofsky, A. (2014). Misbehaving science: Controversy and the development of behavior genetics. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • Prell, C. (2012). Social network analysis: History, theory, & methodology. Thousand Oaks: Sage.

    Google Scholar 

  • Price de Solla, D. J. (1963). Little science, big science. New York: Columbia University Press.

    Google Scholar 

  • Robins, G. (2011). Exponential random graph models for social networks. In eds. John Scott and Peter Carrington.: 484–500.

  • Robins, G. (2015). Doing social network research: Network-based research design for social scientists. Thousand Oaks: SAGE.

    Google Scholar 

  • Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (P*) models for social networks. Social Networks, 29(2), 173–191.

    Article  Google Scholar 

  • Rojas, F. (2007). From black power to black studies: How a radical social movement became an academic discipline. Baltimore: JHU Press.

    Google Scholar 

  • Škerlavaj, M., Dimovski, V., & Desouza, K. C. (2010). Patterns and structures of intra-organizational learning networks within a knowledge-intensive organization. Journal of Information Technology, 25(2), 189–204.

    Article  Google Scholar 

  • Skupin, A., Biberstine, J., & Börner, K. (2013). Visualizing the topical structure of the medical sciences: A self-organizing map approach. PLoS ONE, 8(3), e58779.

    Article  Google Scholar 

  • Small, M. L. (1999). Department conditions and the emergence of new disciplines: Two cases in the legitimation of African-American studies. Theory and Society, 28(5), 659–707.

    Article  Google Scholar 

  • Snow, C. P. (1959). The two cultures. Cambridge: Cambridge University Press.

    Google Scholar 

  • Stigler, S. M. (1994). Citation patterns in the journals of statistics and probability. Statistical Science, 9, 94–108.

    Article  Google Scholar 

  • Su, C., & Contractor, N. (2011). A multidimensional network approach to studying team members’ information seeking from human and digital knowledge sources in consulting firms. Journal of the American Society for Information Science and Technology, 62(7), 1257–1275.

    Article  Google Scholar 

  • Turk-Bicakci, L. (2007). The development of social movement programs and departments in higher education: Women’s and ethnic studies from 1975 to 2000. Ph.D. thesis. University of California Riverside.

  • Van Leeuwen, T., & Robert, T. (2000). Interdisciplinary dynamics of modern science: Analysis of cross-disciplinary citation flows. Research Evaluation, 9(3), 183–187.

    Article  Google Scholar 

  • Vitanov, N. K, & Ausloos, M. R. (2012). Knowledge epidemics and population dynamics models for describing idea diffusion. In: A. Scharnhorst, K. Börner & P. van den Besselaar (Eds.), Models of science dynamics: Encounters between complexity theory and information sciences (pp. 69–125). Berlin: Springer.

    Chapter  Google Scholar 

  • Wilson, D. S., & Sober, E. (1994). Reintroducing group selection to the human behavioral sciences. Behavioral and Brain Sciences, 17(04), 585–608.

    Article  Google Scholar 

  • Wray, B., & Bornmann, L. (2015). Philosophy of science viewed through the lense of ‘referenced publication years spectroscopy’ (RPYS). Scientometrics, 102(3), 1987–1996.

    Article  Google Scholar 

  • Yan, E. (2014). Finding knowledge paths among scientific disciplines. Journal of the Association for Information Science and Technology, 65(11), 2331–2347.

    Article  Google Scholar 

  • Yan, E., Ding, Y., Cronin, B., & Leydesdorff, L. (2013). A bird’s-eye view of scientific trading: Dependency relations among fields of science. Journal of Informetrics, 7(2), 249–264.

    Article  Google Scholar 

  • Yu, G., Wang, M.-Y., & Da-Ren, Y. (2010). Characterizing knowledge diffusion of nanoscience & nanotechnology by citation analysis. Scientometrics, 84(1), 81–97.

    Article  Google Scholar 

  • Zhu, Y., & Yan, E. (2015). Dynamic subfield analysis of disciplines: An examination of the trading impact and knowledge diffusion patterns of computer science. Scientometrics, 104(1), 335–359.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Knowledge Integration, Department of Sociology and Legal Studies, University of Waterloo, Waterloo, ON, Canada

    John McLevey

  2. Department of Sociology and Legal Studies, University of Waterloo, Waterloo, ON, Canada

    Alexander V. Graham & Pierson Browne

  3. Computational Social Science Program, Division of the Social Sciences, University of Chicago, Chicago, IL, USA

    Reid McIlroy-Young

  4. Department of Knowledge Integration, Department of Philosophy, University of Waterloo, Waterloo, ON, Canada

    Kathryn S. Plaisance

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Correspondence to John McLevey.

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McLevey, J., Graham, A.V., McIlroy-Young, R. et al. Interdisciplinarity and insularity in the diffusion of knowledge: an analysis of disciplinary boundaries between philosophy of science and the sciences. Scientometrics 117, 331–349 (2018). https://doi.org/10.1007/s11192-018-2866-8

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