The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.
Intersectional inequalities in science
Open accessAbstract
Significance: The US scientific workforce is not representative of the population. Barriers to entry and participation have been well-studied; however, few have examined the effect of these disparities on the advancement of science. Furthermore, most studies have looked at either race or gender, failing to account for the intersection of these variables. Our analysis utilizes millions of scientific papers to study the relationship between scientists and the science they produce. We find a strong relationship between the characteristics of scientists and their research topics, suggesting that diversity changes the scientific portfolio with consequences for career advancement for minoritized individuals. Science policies should consider this relationship to increase equitable participation in the scientific workforce and thereby improve the robustness of science.
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Cites
Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color
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Structural racism and health inequities in the USA: evidence and interventions
Bailey, Z. D., Krieger, N., Agénor, M., Graves, J., Linos, N., & Bassett, M. T. (2017) · The Lancet · doi:10.1016/s0140-6736(17)30569-x · Cited by 5,203
The Sociology of Science: Theoretical and Empirical Investigations
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Science faculty’s subtle gender biases favor male students
Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1211286109 · Cited by 3,150
Women, Minorities, and Persons with Disabilities in Science and Engineering: 2013: (558442013-001)
(2013) · PsycEXTRA Dataset · doi:10.1037/e558442013-001 · Cited by 2,471
Intersectionality's Definitional Dilemmas
Collins, P. H. (2015) · Annual Review of Sociology · doi:10.1146/annurev-soc-073014-112142 · Cited by 2,419
Racism and health: evidence and needed research
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Cited by
Artificial intelligence and illusions of understanding in scientific research
Messeri, L., & Crockett, M. J. (2024) · Nature · doi:10.1038/s41586-024-07146-0 · Cited by 707
Socioeconomic roots of academic faculty
Morgan, A. C., LaBerge, N., Larremore, D. B., Galesic, M., Brand, J. E., & Clauset, A. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01425-4 · Cited by 269
Systemic racial disparities in funding rates at the National Science Foundation
Chen, C. Y., Kahanamoku, S. S., Tripati, A., Alegado, R. A., Morris, V. R., Andrade, K., & Hosbey, J. (2022) · eLife · doi:10.7554/elife.83071 · Cited by 127
Why and how to incorporate issues of race/ethnicity and gender in research integrity education
Rasmussen, L. M. (2024) · Accountability in Research · doi:10.1080/08989621.2023.2239145 · Cited by 6
“While broader inclusion of researchers improves the range of topics researched, the same study found that the authors conducting this work are sometimes disadvantaged by their topics in terms of citations (Kozlowski et al. 2022).”
Reviewer2: Optimizing Review Generation Through Prompt Generation
Gao, Z., Brantley, K., & Joachims, T. (2024) · arXiv · doi:10.48550/arxiv.2402.10886 · Cited by 3
Metadata
- Authors
- Kozlowski, D., Larivière, V., Sugimoto, C. R., & Monroe-White, T.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2022-01-04
- Volume
- 119
- Issue
- 2
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W4205475170
- Cited by
- 312
- References
- 86
- FWCI
- 39.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Kozlowski, D., Larivière, V., Sugimoto, C. R., & Monroe-White, T. (2022). Intersectional inequalities in science. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2113067119
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.
