JOURNAL ARTICLE
Suspicious places make people suspicious: Officers' perceptions of place‐based conditions in racialized drug enforcement.
Published In: Criminology & Public Policy, 2023, v. 22, n. 1. P. 63 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Gaston, Shytierra; Brunson, Rod K.; Ayeni, David O. 3 of 3
Abstract
Research Summary: Place‐based conditions are well‐established predictors of police behavior, but the literature lacks nuanced examinations of how place‐based factors influence officer decision making, especially by citizen race/ethnicity and from officers' perspectives. We investigate officers' accounts regarding how they weigh place‐based factors into their arrest decisions of Black, Hispanic, and White drug suspects in Newark, New Jersey from 2011 to 2016. Our analysis of 438 filed drug arrest reports revealed that most arrestees, especially Black Americans, became susceptible to heightened police scrutiny because of their presence in stigmatized, criminalized areas. Although place‐based stigma and individualized prohibited behavior coalesced to guide police contacts with Hispanic and White residents, officers made contacts with Black Americans based on a lower legal basis, often irrespective of their individualized behavior in stigmatized places. Policy Implications: Officers' differential, racialized reliance on place‐based conditions supports the need for effective, evidence‐based, community‐centered social services that reduce crime, overreliance on police, and opportunities for discriminatory policing. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Criminology & Public Policy. 2023/02, Vol. 22, Issue 1, p63
- Document Type:Article
- Subject Area:Law
- Publication Date:2023
- ISSN:1538-6473
- DOI:10.1111/1745-9133.12606
- Accession Number:161968427
- Copyright Statement:Copyright of Criminology & Public Policy 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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