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

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  • Mourtgos, Scott M.
  • Adams, Ian T.

Abstract

•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

Suggested Citation

  • Mourtgos, Scott M. & Adams, Ian T., 2019. "The rhetoric of de-policing: Evaluating open-ended survey responses from police officers with machine learning-based structural topic modeling," Journal of Criminal Justice, Elsevier, vol. 64(C), pages 1-1.
  • Handle: RePEc:eee:jcjust:v:64:y:2019:i:c:1
    DOI: 10.1016/j.jcrimjus.2019.101627
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    References listed on IDEAS

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    1. Mourtgos, Scott M. & Adams, Ian T. & Mastracci, Sharon H., 2021. "Improving victim engagement and officer response in rape investigations: A longitudinal assessment of a brief training," Journal of Criminal Justice, Elsevier, vol. 74(C).

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