JOURNAL ARTICLE

Who Asked You? The (Dis)Use of Questions Presented at the U.S. Supreme Court.

  • Published In: Political Science Quarterly (Oxford University Press / USA), 2024, v. 139, n. 1. P. 35 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wofford, Claire; Krell, Matthew Reid 3 of 3

Abstract

This article examines how the United States Supreme Court responds to the "Questions Presented" (QPs) in petitions for writs of certiorari, focusing on whether the Court accepts, alters, or replaces the legal questions framed by litigants when it agrees to hear a case. Analyzing data from 2010 to 2020, the study finds that the Court is predominantly passive, accepting petitioners’ QPs without modification in about 76% of cases, though it alters or substitutes questions in roughly 24% of instances. The Court’s likelihood to modify QPs varies with ideological alignment between the Court and petitioner and the petitioner’s identity, with local governments, women and minorities, and interest groups less likely to have their questions accepted unchanged compared to business petitioners; the federal government’s framing success is comparable to businesses. The findings highlight the significant role litigants play in shaping the Court’s agenda and legal policymaking, while also revealing that the Court exercises selective agenda-setting power, especially in salient cases where it is more prone to substitute its own questions.

Additional Information

  • Source:Political Science Quarterly (Oxford University Press / USA). 2024/03, Vol. 139, Issue 1, p35
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2024
  • ISSN:0032-3195
  • DOI:10.1093/psquar/qqad130
  • Accession Number:176131638
  • Copyright Statement:Copyright of Political Science Quarterly (Oxford University Press / USA) 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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