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

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. 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. 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. 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. 4.

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

  5. 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. 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. 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. 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. 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. 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. 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. 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. 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.

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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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Keywords

  • Disciplines
  • Intellectual networks
  • Exponential random graph models
  • Diffusion
  • Citations
  • Sociology of science
  • Science of science
  • Philosophy of science