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Structural causes of citation gaps

Abstract

The social identity of a researcher can affect their position in a community, as well as the uptake of their ideas. In many fields, members of underrepresented or minority groups are less likely to be cited, leading to citation gaps. Though this empirical phenomenon has been well-studied, empirical work generally does not provide insight into the causes of citation gaps. I will argue, using mathematical models, that citation gaps are likely due in part to the structure of academic communities. The existence of these ‘structural causes’ has implications for attempts to lessen citation gaps, and for proposals to make academic communities more efficient (e.g. by eliminating pre-publication peer review). These proposals have the potential to create feedback loops, amplifying current structural inequities.

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Notes

  1. There are a variety of reasons why we might not expect gender citation gaps in every field. See Sect. 3.4 for further discussion the factors relevant to the existence of citation gaps.

  2. In calling this a ‘structural cause’, I am using the phrase to indicate that the cause pertains to how the group is organized, or that the relations between the parts within the whole are important. The use of ‘structural’ in this phrase should not be taken to encompass all types of causes that are referred to as ‘structural’ (e.g. structural oppression or systemic discrimination), nor to preclude there being being a myriad of such structural causes present in the community. Thanks to a reviewer for pointing out this possible confusion.

  3. Building on Rubin and Schneider (2020), this paper can be thought of as demonstrating particular mechanisms regarding credit attribution within the ‘credit economy’ by which these inequities can emerge.

  4. A copy of the code for this paper has been made available on the Open Science Framework at https://osf.io/9suty/?view_only=70022ee795654ee9a22bc311a97a763e.

  5. The results presented here are for \(p=.3\), but similar results can be obtained with higher or lower probabilities.

  6. To get a reliable estimate of the expected citation gap, 100 different networks were formed for each combination of p(in) and majority group size, and 100 simulations of the citation process were run on each (with authors of the original papers chosen at random each time).

  7. In a model which excludes the two original authors from the accumulation of citations, results show that this decrease does not occur.

  8. For the results presented here, the likelihood to cite a paper based on a search, p is determined by the page, g, such that \(p= .95^{10g}\) and \(g = 10 - \frac{10c}{10+c}\), where c is the number of citation a paper has accumulated. Nothing depends on these particular equations, they merely capture the observation that more citations lead a paper to be on an earlier page, consequently making it more likely to be cited.

  9. One reason for this is that people are more likely to find a paper to cite through their network than by searching through pages of internet searches, so slightly more than 30% of citations come from looking through the network. For example, again looking at the extreme of high homophily and low representation, data from these simulations shows that around 40% of citations come from looking through the network. However, this cannot fully explain the results in Fig. 3. If it did, we would expect about 60% reduction in the majority’s advantage (from .34 to .14).

  10. Of course, minority group members may also have some chance of not citing other minority group members. This will affect overall citation rates in a similar way.

  11. The magnitude of the effect of bias or publication rates on the citation gap may be different, depending on the values those parameters take.

  12. Thanks to Chris Weaver and Riet Van Bork for discussions on this topic. See also Rubin and Schneider (2020) for a discussion of the role signaling can play in the context of assigning priority for scientific discoveries.

  13. Thanks to Mike Schneider for discussions on this point.

  14. See Bright (2017) for a decision theoretic model supporting this argument.

  15. Thanks to a reviewer for pushing me to clarify the points of agreement and disagreement.

  16. The method of forming these networks should not make a difference to the results, as long as we form a homophilic weighted directed network.

  17. Turn order each round is determined randomly, and the chance to publish an additional paper was set to 10% for the results below.

  18. The five papers were chosen by a weighted random sampling procedure. It is possible for a researcher to engage with a paper in multiple ways, e.g. by commenting on it and by sharing it with others.

  19. For the results presented here, this increase is .005, but the exact amount does not change the qualitative results. Additionally, weights were normalized at the start of the simulation and each time they evolve, so that the sum of each person’s outgoing arrows is one.

  20. Results are similar for other measures of centrality, e.g. closeness centrality, which is based on shortest path lengths between nodes.

  21. To get an estimate of how the process is expected to go, for each combination of parameters, 50 networks were formed randomly and five simulations were run on each of these networks. Data points in Fig. 5 represent each network that was formed, averaging over the five simulations. Results are very similar if instead each simulation is considered a data point.

  22. This is not to disparage those who make a concerted effort to be cognizant of citation gaps when compiling bibliographies, or journals which have made efforts to encourage authors to be cognizant. As emphasized, this paper does not deny the existence of individual level causes, such as implicit bias, which these efforts may counterbalance effectively.

  23. See Schneider et al. (2020) for an argument that exchange of ideas between social identity groups is epistemically important.

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Acknowledgements

Many thanks to Mike Schneider for ideas, discussions, criticisms, and so on. The initial models here are similar to those developed in a paper co-authored with Mike (Priority and Privilege in Scientific Discovery). Thanks to Justin Bruner, the Spring 2021 Fellows at the Pitt Center for Philosophy of Science, and two reviewers for comments on the paper. Also, thanks to audiences at University of Groningen, the Women in Academia conference at UC Irvine, and the Pitt Center for Philosophy of Science lunchtime talk for feedback. This material is based upon work supported by the National Science Foundation under Grant No. 2045007.

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Correspondence to Hannah Rubin.

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Rubin, H. Structural causes of citation gaps. Philos Stud 179, 2323–2345 (2022). https://doi.org/10.1007/s11098-021-01765-3

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Keywords

  • Philosophy of science
  • Structural inequity
  • Citation gaps
  • Formal models of science