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Cited by 282

The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.

Gendered Perceptions and Political Candidacies: A Central Barrier to Women's Equality in Electoral Politics

Open access
Fox, Richard L., Lawless, Jennifer L.·2011·American Journal of Political Science

Abstract

Based on the second wave of the Citizen Political Ambition Panel Study, we provide the first thorough analysis of how gender affects women and men's efficacy to run for office. Our findings reveal that, despite comparable credentials, backgrounds, and experiences, accomplished women are substantially less likely than similarly situated men to perceive themselves as qualified to seek office. Importantly, women and men rely on the same factors when evaluating themselves as candidates, but women are less likely than men to believe they meet these criteria. Not only are women more likely than men to doubt that they have skills and traits necessary for electoral politics, but they are also more likely to doubt their abilities to engage in campaign mechanics. These findings are critical because the perceptual differences we uncover account for much of the gender gap in potential candidates' self-efficacy and ultimately hinder women's prospects for political equality.

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Metadata

Authors
Fox, R. L., & Lawless, J. L.
Venue
American Journal of Political Science
Publisher
Wiley
Published
2010-10-25
Volume
55
Issue
1
Pages
59-73
ISSN
0092-5853 (ISSN-L)
OpenAlex
W3124016754
Cited by
282
References
43
FWCI
7.12
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Gendered Perceptions and Political Candidacies: A Central Barrier to Women's Equality in Electoral Politics. https://app.cochat.ai/papers/10.1111/j.1540-5907.2010.00484.x
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Fox, R. L., & Lawless, J. L. (2011). Gendered Perceptions and Political Candidacies: A Central Barrier to Women's Equality in Electoral Politics. American Journal of Political Science. https://doi.org/10.1111/j.1540-5907.2010.00484.x

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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.