JOURNAL ARTICLE

Community-oriented copaganda: Anti-Black violence in a visual archive of policing.

  • Published In: Crime, Media, Culture, 2025, v. 21, n. 1. P. 96 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Petersen, Amanda M 3 of 3

Abstract

This article critically examines the Community Policing in Action Photo Contest archive, curated by the Office of Community Oriented Policing Services (COPS Office) under the U.S. Department of Justice, as a form of "community-oriented copaganda"—a visual strategy that seeks to rebuild police legitimacy by portraying positive interactions between police and racialized communities, particularly Black individuals. Through a visual criminology lens and drawing on critical race theory and Black feminist thought, the analysis reveals that the archive simultaneously obscures and reproduces the racialized violence inherent in policing, using images of Black community members and officers in scenes of "fun and frolic" and paternalistic care to pacify resistance and legitimize state control. The article situates community-oriented policing not as a solution to police legitimacy crises but as a continuation of anti-Black state violence and domestic counterinsurgency, where Blackness is instrumentalized to promote compliance and obscure systemic harm. Ultimately, the archive is interpreted as an active expression of police power that reinforces racial hierarchies and challenges simplistic narratives of policing as benevolent or race-neutral.

Additional Information

  • Source:Crime, Media, Culture. 2025/01, Vol. 21, Issue 1, p96
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2025
  • ISSN:1741-6590
  • DOI:10.1177/17416590241231904
  • Accession Number:182046910
  • Copyright Statement:Copyright of Crime, Media, Culture is the property of Sage Publications Inc. 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.