JOURNAL ARTICLE

Gendered Perceptions of Personality Traits, Policy Stereotypes, and Support for Right-Wing Populist Parties: Lessons from Turkey.

  • Published In: Social Politics: International Studies in Gender, State & Society, 2023, v. 30, n. 1. P. 290 1 of 3

  • Database: Sociology Source Ultimate 2 of 3

  • Authored By: Yildirim, Tevfik Murat; Bulut, Alper Tolga 3 of 3

Abstract

This article investigates the relationship between gender-based personality and policy stereotypes and support for right-wing populist parties, focusing on Turkey's incumbent Justice and Development Party (AKP) and the main opposition Republican People's Party (CHP). Using a 2019 nationwide survey, the study finds that voters who believe men are better suited to handle traditionally male policy areas (e.g., economy, national security, crime) and possess male-typed traits (e.g., self-confidence, authoritativeness) are more likely to support the AKP, which promotes a gender-inegalitarian discourse emphasizing traditional female roles. Conversely, CHP supporters tend to endorse female policy competencies (e.g., education, healthcare, family services) and reject male-typed traits and policy stereotypes. The findings suggest that gendered perceptions of competence and traits significantly correlate with voting behavior in Turkey's polarized political context, reflecting broader dynamics between populist politics and gender stereotypes.

Additional Information

  • Source:Social Politics: International Studies in Gender, State & Society. 2023/03, Vol. 30, Issue 1, p290
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:1072-4745
  • DOI:10.1093/sp/jxac007
  • Accession Number:162503298
  • Copyright Statement:Copyright of Social Politics: International Studies in Gender, State & Society is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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