JOURNAL ARTICLE
Since you put it that way... Gender norms and interruptions at Supreme Court oral arguments.
Published In: Social Science Quarterly (Wiley-Blackwell), 2024, v. 105, n. 3. P. 582 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Gleason, Shane A. 3 of 3
Abstract
Objective: At U.S. Supreme Court oral arguments, female attorneys are more likely to be interrupted than their male counterparts under some conditions. This makes it difficult for women to effectively construct a narrative and substantively impact case law. While existing work conceptualizes gender as a binary, I draw on recent work stressing gender is performative to deesentialize gender and explore how attorneys' compliance with gender norms and subtle expectations about men's and women's behavior in a host of contexts, predicts interruptions at oral arguments. Methods: Via quantitative textual analysis of all oral arguments from 2004 to 2019 where one attorney argues for the petitioner and one for the respondent, I examine the extent to which gender norm compliance predicts interruptions. Results: I find both male and female attorneys are interrupted more frequently when their oral arguments are not gender normative. Thus, an argument that is successful for a male attorney is not necessarily successful for a female attorney, and vice versa. Conclusion: My results underscore female attorneys are not less successful as a matter of course; attorney success is driven by attorney compliance with gender norms. This work also raises a number of normative questions I encourage future scholars to explore. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Social Science Quarterly (Wiley-Blackwell). 2024/05, Vol. 105, Issue 3, p582
- Document Type:Article
- Subject Area:Law
- Publication Date:2024
- ISSN:0038-4941
- DOI:10.1111/ssqu.13377
- Accession Number:177532335
- Copyright Statement:Copyright of Social Science Quarterly (Wiley-Blackwell) is the property of Wiley-Blackwell 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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