The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Historical comparison of gender inequality in scientific careers across countries and disciplines
Open accessAbstract
Significance: Empirical evidence suggests significant gender differences in the total productivity and impact of academic careers across science, technology, engineering, and mathematics (STEM) fields. Paradoxically, the increase in the number of women academics over the past 60 years has increased these gender differences. Yet, we find that men and women publish a comparable number of papers per year and have equivalent career-wise impact for the same total number of publications. This suggests the productivity and impact of gender differences are explained by different publishing career lengths and dropout rates. This comprehensive picture of gender inequality in academic publishing can help rephrase the conversation around the sustainability of women’s careers in academia, with important consequences for institutions and policy makers.
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Cites
Power-Law Distributions in Empirical Data
Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009) · SIAM Review · doi:10.1137/070710111 · Cited by 8,019
Power-law distributions in empirical data
Clauset, A. J., Shalizi, C. R., & Newman, M. E. J. (2018) · Figshare · doi:10.1184/r1/6586835.v1 · Cited by 6,504
The journal coverage of Web of Science and Scopus: A comparative analysis
Mongeon, P., & Paul-Hus, A. (2016) · Scientometrics · doi:10.1007/s11192-015-1765-5 · Cited by 5,274
Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., Petersen, A. M., Radicchi, F., Sinatra, R., Uzzi, B., et al. (2018) · Science · doi:10.1126/science.aao0185 · Cited by 1,497
Self-citationBibliometrics: Global gender disparities in science
Larivière, V., Ni, C., Gingras, Y., Cronin, B., & Sugimoto, C. R. (2013) · Nature · doi:10.1038/504211a · Cited by 1,493
Nepotism and sexism in peer-review
Wennerås, C., & Wold, A. (1997) · Nature · doi:10.1038/387341a0 · Cited by 1,345
Eagly, A. H., Nater, C., Miller, D. I., Kaufmann, M., & Sczesny, S. (2020) · American Psychologist · doi:10.1037/amp0000494 · Cited by 1,136
Understanding current causes of women's underrepresentation in science
Ceci, S. J., & Williams, W. M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1014871108 · Cited by 1,114
Aybar, A. (2017) · Revista de Sociología · doi:10.15381/rsoc.v0i25.19057 · Cited by 907
The Role of Gender in Scholarly Authorship
West, J. D., Jacquet, J., King, M. M., Correll, S. J., & Bergstrom, C. T. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066212 · Cited by 892
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Metadata
- Authors
- Huang, J., Gates, A. J., Sinatra, R., & Barabási, A.-L.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2020-02-18
- Volume
- 117
- Issue
- 9
- Pages
- 4609-4616
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W3006753318
- Cited by
- 954
- References
- 59
- FWCI
- 84.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.