The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
The gender gap in science: How long until women are equally represented?
Open accessAbstract
Women comprise a minority of the Science, Technology, Engineering, Mathematics, and Medicine (STEMM) workforce. Quantifying the gender gap may identify fields that will not reach parity without intervention, reveal underappreciated biases, and inform benchmarks for gender balance among conference speakers, editors, and hiring committees. Using the PubMed and arXiv databases, we estimated the gender of 36 million authors from >100 countries publishing in >6000 journals, covering most STEMM disciplines over the last 15 years, and made a web app allowing easy access to the data (https://lukeholman.github.io/genderGap/). Despite recent progress, the gender gap appears likely to persist for generations, particularly in surgery, computer science, physics, and maths. The gap is especially large in authorship positions associated with seniority, and prestigious journals have fewer women authors. Additionally, we estimate that men are invited by journals to submit papers at approximately double the rate of women. Wealthy countries, notably Japan, Germany, and Switzerland, had fewer women authors than poorer ones. We conclude that the STEMM gender gap will not close without further reforms in education, mentoring, and academic publishing.
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Cites
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Metadata
- Authors
- Holman, L., Stuart-Fox, D., & Hauser, C. E.
- Venue
- PLOS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-04-19
- Volume
- 16
- Issue
- 4
- Pages
- e2004956
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- OpenAlex
- W2800701210
- Cited by
- 769
- References
- 26
- FWCI
- 149.33
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
