JOURNAL ARTICLE

Armed Federalism, Gun Markets, and the Right to Bear Arms in the United States.

  • Published In: Publius: The Journal of Federalism, 2024, v. 54, n. 3. P. 534 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Obert, Jonathan 3 of 3

Abstract

This article examines the fragmented and varied regulatory, cultural, and electoral responses to guns and gun rights in the United States through the lens of two enduring features: "armed federalism," a constitutional and institutional tradition distributing the right to use violence across multiple public and private actors, and the development of a unique, nationally integrated domestic market for small firearms. It argues that these factors have produced a patchwork of local, state, and federal gun regulations, fostered the rise of a national gun-rights movement—most notably led by the National Rifle Association (NRA)—and culminated in a legal strategy of gun-rights constitutionalism affirmed by key Supreme Court rulings such as District of Columbia v. Heller (2008). The article highlights how armed federalism’s cultural and institutional logic, combined with the widespread availability of firearms through a consumer market, underpins the complex and often contradictory nature of American gun politics, shaped by regional, racial, and social differences. It concludes that despite ongoing political and organizational challenges, these structural conditions continue to sustain gun rights mobilization and the contested place of firearms in U.S. political life.

Additional Information

  • Source:Publius: The Journal of Federalism. 2024/07, Vol. 54, Issue 3, p534
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:0048-5950
  • DOI:10.1093/publius/pjae020
  • Accession Number:178608395
  • Copyright Statement:Copyright of Publius: The Journal of Federalism 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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