The extent and drivers of gender imbalance in neuroscience reference lists

Abstract

Similarly to many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances. Although publishing and conference participation are often highlighted, recent research has called attention to the prevalence of gender imbalance in citations. Because of the downstream effects of citations on visibility and career advancement, understanding the role of gender in citation practices is vital for addressing scientific inequity. Here, we investigate whether gendered patterns are present in neuroscience citations. Using data from five top neuroscience journals, we find that reference lists tend to include more papers with men as first and last author than would be expected if gender were unrelated to referencing. Importantly, we show that this imbalance is driven largely by the citation practices of men and is increasing over time as the field diversifies. We assess and discuss possible mechanisms and consider how researchers might approach these issues in their own work.

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Fig. 1: Trends in author gender within top neuroscience journals between 1995 and 2018.
Fig. 2: Construction and visualization of overall over- and undercitation measures.
Fig. 3: Relationship between author gender and gendered citation practices.
Fig. 4: Temporal trends in citation rates across gender of the cited and citing author.
Fig. 5: Visualization of co-authorship network composition measures.
Fig. 6: Article-level overcitation of MM papers before and after accounting for local network composition.

Data availability

Data and materials for this study have been deposited in an Open Science Framework repository and can be accessed at https://osf.io/h79g8/.

Code availability

Code for reproducing presented estimates and figures can be accessed at https://osf.io/h79g8/, and code that demonstrates the full sampling, processing and analysis pipeline can be accessed at https://github.com/jdwor/gendercitation.

References

  1. 1.

    Holman, L., Stuart-Fox, D. & Hauser, C. E. The gender gap in science: how long until women are equally represented? PLoS Biol. 16, e2004956 (2018).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J. & Handelsman, J. Science faculty’s subtle gender biases favor male students. Proc. Natl Acad. Sci. USA 109, 16474–16479 (2012).

    CAS  Google Scholar 

  3. 3.

    Reshma, J. Sex differences in attainment of independent funding by career development awardees. Ann. Intern. Med. 151, 804–811 (2009).

    Google Scholar 

  4. 4.

    van der Lee, R. & Ellemers, N. Gender contributes to personal research funding success in the Netherlands. Proc. Natl Acad. Sci. USA 112, 12349–12353 (2015).

    PubMed  Google Scholar 

  5. 5.

    Sarsons, H. Recognition for group work: gender differences in academia. Am. Econ. Rev. 107, 141–145 (2017).

    Google Scholar 

  6. 6.

    MacNell, L., Driscoll, A. & Hunt, A. N. What’s in a name: exposing gender bias in student ratings of teaching. Innov. Higher Educ. 40, 291–303 (2015).

    Google Scholar 

  7. 7.

    Mengel, F., Sauermann, J. & Zölitz, U. Gender bias in teaching evaluations. J. Eur. Econ. Assoc. 17, 535–566 (2019).

    Google Scholar 

  8. 8.

    Boring, A. Gender biases in student evaluations of teaching. J. Public Econ. 145, 27–41 (2017).

    Google Scholar 

  9. 9.

    Nielsen, M. W. Limits to meritocracy? Gender in academic recruitment and promotion processes. Sci. Pub. Pol. 43, 386–399 (2016).

    Google Scholar 

  10. 10.

    De Paola, M. & Scoppa, V. Gender discrimination and evaluators’ gender: evidence from Italian academia. Economica 82, 162–188 (2015).

    Google Scholar 

  11. 11.

    West, J. D., Jacquet, J., King, M. M., Correll, S. J. & Bergstrom, C. T. The role of gender in scholarly authorship. PLoS ONE 8, e66212 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Wilhelm, I., Conklin, S. L. & Hassoun, N. New data on the representation of women in philosophy journals: 2004–2015. Int. J. Philos. Stud. 175, 1441–1464 (2018).

    Google Scholar 

  13. 13.

    Huang, J., Gates, A. J., Sinatra, R. & Barabasi, A.-L. Historical comparison of gender inequality in scientific careers across countries and disciplines. Proc. Natl Acad. Sci. USA 117, 4609–4616 (2020).

    CAS  PubMed  Google Scholar 

  14. 14.

    Ferber, M. A. & Brun, M. The gender gap in citations: does it persist? Fem. Econ. 17, 151–158 (2011).

    Google Scholar 

  15. 15.

    Maliniak, D., Powers, R. & Walter, B. F. The gender citation gap in international relations. Int. Organ. 67, 889–922 (2013).

    Google Scholar 

  16. 16.

    Caplar, N., Tacchella, S. & Birrer, S. Quantitative evaluation of gender bias in astronomical publications from citation counts. Nat. Astron. 1, 0141 (2017).

    Google Scholar 

  17. 17.

    Fang, D., Moy, E., Colburn, L. & Hurley, J. Racial and ethnic disparities in faculty promotion in academic medicine. JAMA 284, 1085–1092 (2000).

    CAS  PubMed  Google Scholar 

  18. 18.

    Petersen, A. M. et al. Reputation and impact in academic careers. Proc. Natl Acad. Sci. USA 111, 15316–15321 (2014).

    CAS  PubMed  Google Scholar 

  19. 19.

    Way, S. F., Morgan, A. C., Larremore, D. B. & Clauset, A. Productivity, prominence and the effects of academic environment. Proc. Natl Acad. Sci. USA 116, 10729–10733 (2019).

    CAS  PubMed  Google Scholar 

  20. 20.

    Joels, M. & Mason, C. A tale of two sexes. Neuron 82, 1196–1199 (2014).

    CAS  PubMed  Google Scholar 

  21. 21.

    Anonymous. Promoting diversity in neuroscience. Nat. Neurosci. 21, 1 (2018).

  22. 22.

    Schrouff, J. et al. Gender bias in (neuro)science: facts, consequences and solutions. Eur. J. Neurosci. 50, 3094–3100 (2019).

    PubMed  Google Scholar 

  23. 23.

    Chakravartty, P., Kuo, R., Grubbs, V. & McIlwain, C. #CommunicationSoWhite. J. Commun. 68, 254–266 (2018).

    Google Scholar 

  24. 24.

    Thiem, Y., Sealey, K. F., Ferrer, A. E., Trott, A. M. & Kennison, R. Just Ideas? The Status and Future of Publication Ethics in Philosophy (Publication Ethics, 2018).

  25. 25.

    Dion, M. L., Sumner, J. L. & Mitchell, S. M. Gendered citation patterns across political science and social science methodology fields. Polit. Anal. 26, 312–327 (2018).

    Google Scholar 

  26. 26.

    Rossiter, M. W. The Matthew Matilda effect in science. Soc. Stud. Sci. 23, 325–341 (1993).

    Google Scholar 

  27. 27.

    Mitchell, S. M., Lange, S. & Brus, H. Gendered citation patterns in international relations journals. Int. Stud. Perspect. 14, 485–492 (2013).

    Google Scholar 

  28. 28.

    Bergstrom, C. T., West, J. D. & Wiseman, M. A. The Eigenfactor metrics. J. Neurosci. 28, 11433–11434 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Feder, E. K. Making Sense of Intersex: Changing Ethical Perspectives in Biomedicine (Indiana University Press, 2014).

  30. 30.

    Stryker, S. Transgender History (Seal Studies) (Seal Press, 2008).

  31. 31.

    Bertrand, M. & Mullainathan, S. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. Am. Econ. Rev. 94, 991–1013 (2004).

    Google Scholar 

  32. 32.

    Brownstein, M. Implicit bias. in The Stanford Encyclopedia of Philosophy. Fall 2019 edn. (ed. Zalta, E. N.) https://plato.stanford.edu/entries/implicit-bias/ (Stanford University, 2019).

  33. 33.

    Holman, L. & Morandin, C. Researchers collaborate with same-gendered colleagues more often than expected across the life sciences. PLoS ONE 14, e0216128 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Lee, E. et al. Homophily and minority-group size explain perception biases in social networks. Nat. Hum. Behav. 3, 1078–1087 (2019).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Aksnes, D. W., Langfeldt, L. & Wouters, P. Citations, citation indicators and research quality: an overview of basic concepts and theories. SAGE Open 9, 215824401982957 (2019).

    Google Scholar 

  36. 36.

    Henry, P. J. Institutional bias. in Handbook of Prejudice, Stereotyping and Discrimination (eds. Dovidio, J. F. et al.) 426–440 (Sage, 2010).

  37. 37.

    Clarke, J. A. Explicit bias. Northwest. Univ. Law Rev. 113, 505–586 (2018).

    Google Scholar 

  38. 38.

    Conaway, W. & Bethune, S. Implicit bias and first name stereotypes: what are the implications for online instruction? J. Online Learn. 19, 162–178 (2015).

    Google Scholar 

  39. 39.

    Paludi, M. A. & Strayer, L. A. What’s in an author’s name? Differential evaluations of performance as a function of author’s name. Sex Roles 12, 353–361 (1985).

    Google Scholar 

  40. 40.

    Posselt, J. R. Inside Graduate Admissions (Harvard University Press, 2016).

  41. 41.

    Colgan, J. Gender bias in international relations graduate education? New evidence from syllabi. PS Polit. Sci. Polit. 50, 456–460 (2017).

    Google Scholar 

  42. 42.

    Penders, B. Ten simple rules for responsible referencing. PLoS Comput. Biol. 14, e1006036 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Sumner, J. L. The gender balance assessment tool (GBAT): a web-based tool for estimating gender balance in syllabi and bibliographies. PS Polit. Sci. Polit. 51, 396–400 (2018).

    Google Scholar 

  44. 44.

    Lamont, J. & Favor, C. Distributive justice. in The Stanford Encyclopedia of Philosophy. Winter 2017 edn. https://plato.stanford.edu/entries/justice-distributive/ (ed. Zalta E. N.) (Stanford University, 2017).

  45. 45.

    Olsaretti, S. The idea of distributive justice. in The Oxford Handbook of Distributive Justice Vol. 1 (ed. Olsaretti S.) https://doi.org/10.1093/oxfordhb/9780199645121.013.38 (Oxford University Press, 2018).

  46. 46.

    Young, I. M. & Allen, D. S. Justice and the Politics of Difference (Princeton University Press, 2011).

  47. 47.

    Ahmed, S. On Being Included: Racism and Diversity in Institutional Life (Duke University Press, 2012).

  48. 48.

    Walker, M. U. What is Reparative Justice? (The Aquinas Lecture 2010) (Marquette University Press, 2010).

  49. 49.

    Anderson, E. The Imperative of Integration (Princeton University Press, 2010).

  50. 50.

    Gutiérrez, M. G., Niemann, Y. F., González, C. G. & Harris, A. P. (eds.) Presumed Incompetent: The Intersections of Race and Class for Women in Academia (University Press of Colorado, 2012).

  51. 51.

    Toth, C., Durham, E., Kantarcioglu, M., Xue, Y. & Malin, B. SOEMPI: a secure open enterprise master patient index software toolkit for private record linkage. AMIA Annu. Symp. Proc. 2014, 1105–1114 (2014).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Blevins, C. & Mullen, L. Jane, John … Leslie? A historical method for algorithmic gender prediction. Digit. Humanit. Q. 9, 2015.

  53. 53.

    Fausto-Sterling, A. Sexing the Body: Gender Politics and the Construction of Sexuality 1st edn, (Basic Books, 2000).

  54. 54.

    King, M. M., Bergstrom, C. T., Correll, S. J., Jacquet, J. & West, J. D. Men set their own cites high: gender and self-citation across fields and over time. Socius 3, 237802311773890 (2017).

    Google Scholar 

  55. 55.

    Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn, (Chapman and Hall/CRC, 2017).

  56. 56.

    Sture, H. A simple sequentially rejective multiple test procedure. Scand. J. Statist. 6, 65–70 (1979).

    Google Scholar 

  57. 57.

    Jadidi, M., Karimi, F., Lietz, H. & Wagner, C. Gender disparities in science? Dropout, productivity, collaborations and success of male and female computer scientists. Adv. Complex Syst. 21, 1750011 (2018).

    Google Scholar 

  58. 58.

    Yang, Y., Chawla, N. V. & Uzzi, B. A network’s gender composition and communication pattern predict women’s leadership success. Proc. Natl Acad. Sci. USA 116, 2033–2038 (2019).

    CAS  PubMed  Google Scholar 

  59. 59.

    AlShebli, B. K., Rahwan, T. & Woon, W. L. The preeminence of ethnic diversity in scientific collaboration. Nat. Commun. 9, 5163 (2018).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Uhly, K. M., Visser, L. M. & Zippel, K. S. Gendered patterns in international research collaborations in academia. Stud. High. Educ. 42, 1–23 https://doi.org/10.1080/03075079.2015.1072151 (2015).

  61. 61.

    Zippel, K. S. Women in Global Science: Advancing Academic Careers Through International Collaboration (Stanford University Press, 2017).

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Acknowledgements

We thank D. Lydon-Staley and D. Zhou for constructive comments on an earlier version of this manuscript. R.T.S. would like to acknowledge support from the National Institute of Neurological Disorders and Stroke (R01 NS085211 and R01 NS060910). D.S.B. acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation and an NSF CAREER award (PHY-1554488). The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.

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Conceptualization: J.D.D., K.A.L., R.T.S. and D.S.B.; methodology: J.D.D., K.A.L., E.G.T., R.T.S. and D.S.B.; data curation: J.D.D.; formal analysis: J.D.D.; writing: J.D.D., P.Z. and D.S.B. (original draft) and J.D.D., K.A.L., E.G.T., P.Z., R.T.S. and D.S.B. (review and editing); funding acquisition: R.T.S. and D.S.B.; supervision: R.T.S. and D.S.B.

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Correspondence to Danielle S. Bassett.

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Peer review information Nature Neuroscience thanks Clarissa Bauer-Staeb, Katherine Button, Catherine Hobbs, Russell Poldrack and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Dworkin, J.D., Linn, K.A., Teich, E.G. et al. The extent and drivers of gender imbalance in neuroscience reference lists. Nat Neurosci 23, 918–926 (2020). https://doi.org/10.1038/s41593-020-0658-y

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