The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
The underrepresentation of women in science: Differential commitment or the queen bee syndrome?
Open accessAbstract
We examined possible explanations for the underrepresentation of women among university faculty, in two different national contexts. In the Netherlands, a sample of doctoral students (N = 132) revealed no gender differences in work commitment or work satisfaction. Faculty members in the same university (N = 179), however, perceived female students to be less committed to their work and female faculty endorsed these gender‐stereotypical perceptions most strongly. A second study, in Italy, replicated and extended these findings. Again, no gender differences were obtained in the self‐descriptions of male and female doctoral students (N = 80), while especially the female faculty (N = 93) perceived female students as less committed to their work than male students. Additional measures supported an explanation in social identity terms, according to which individual upward mobility (i.e. of female faculty) implies distancing the self from the group stereotype which not only involves perceiving the self as a non‐prototypical group member, but may also elicit stereotypical views of other in‐group members.
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“Minority status also increases competition between members of the minority subgroup (Ellemers et al. 2004).”
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Metadata
- Authors
- Ellemers, N., Van den Heuvel, H., de Gilder, D., Maass, A., & Bonvini, A.
- Venue
- British Journal of Social Psychology
- Publisher
- Wiley
- Published
- 2004-09
- Volume
- 43
- Issue
- 3
- Pages
- 315-338
- ISSN
- 0144-6665 (ISSN-L)
- 2044-8309
- OpenAlex
- W2159878718
- Cited by
- 414
- References
- 61
- FWCI
- 13.85
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
