The rhetoric of de-policing: Evaluating open-ended survey responses from police officers with machine learning-based structural topic modeling. (September 2019)

• Record Type: Journal Article
• Authors: Mourtgos, Scott M.; Adams, Ian T.
• Is Part Of: Journal of criminal justice. Number 64(2019)
• Publication Date: 2019-09
• Subjects: Criminal justice, Administration of -- Periodicals; Justice pénale -- Administration -- Périodiques; 364.05
• DOI: 10.1016/j.jcrimjus.2019.101627
• Languages: English
• ISSNs: 0047-2352

Abstract

Highlights: Machine learning-based textual analysis is a viable tool for police survey research Analyzing large numbers of police free-text responses provides more nuanced understanding of police perceptions of the public Officers' attention to professionalism guards against de-policing, while attention to perceived unfair criticism increases it The public's integrity has a stronger effect on propensity to de-police than the public's knowledge about police work