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


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


•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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    3. Shjarback, John A. & Pyrooz, David C. & Wolfe, Scott E. & Decker, Scott H., 2017. "De-policing and crime in the wake of Ferguson: Racialized changes in the quantity and quality of policing among Missouri police departments," Journal of Criminal Justice, Elsevier, vol. 50(C), pages 42-52.
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    6. Margaret Roberts & Brandon Stewart & Tingley, Dustin, 2014. "stm: R Package for Structural Topic Models," Working Paper 176291, Harvard University OpenScholar.
    7. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    8. Kevin M. Quinn & Burt L. Monroe & Michael Colaresi & Michael H. Crespin & Dragomir R. Radev, 2010. "How to Analyze Political Attention with Minimal Assumptions and Costs," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 209-228, January.
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    Cited by:

    1. Adams, Ian T. & Mourtgos, Scott M. & Nix, Justin, 2023. "Turnover in large US policing agencies following the George Floyd protests," Journal of Criminal Justice, Elsevier, vol. 88(C).
    2. Chung, Ji-Bum & Yeon, Dahye & Kim, Min-Kyu, 2023. "Characteristics of victim blaming related to COVID-19 in South Korea," Social Science & Medicine, Elsevier, vol. 320(C).
    3. Adams, Ian T. & McCrain, Joshua & Schiff, Daniel S. & Schiff, Kaylyn Jackson & Mourtgos, Scott M., 2022. "Public Pressure or Peer Influence: What Shapes Police Executives' Views on Civilian Oversight?," SocArXiv mdu96, Center for Open Science.
    4. Adams, Ian T., 2022. "Modeling Officer Perceptions of Body-worn Cameras: A National Survey," Thesis Commons fnxbg, Center for Open Science.
    5. Lovell, Rachel E. & Klingenstein, Joanna & Du, Jiaxin & Overman, Laura & Sabo, Danielle & Ye, Xinyue & Flannery, Daniel J., 2023. "Using machine learning to assess rape reports: Sentiment analysis detection of officers' “signaling” about victims' credibility," Journal of Criminal Justice, Elsevier, vol. 88(C).
    6. 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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