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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. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Pyrooz, David C. & Decker, Scott H. & Wolfe, Scott E. & Shjarback, John A., 2016. "Was there a Ferguson Effect on crime rates in large U.S. cities?," Journal of Criminal Justice, Elsevier, vol. 46(C), pages 1-8.
    3. Margaret E. Roberts & Brandon M. Stewart & Edoardo M. Airoldi, 2016. "A Model of Text for Experimentation in the Social Sciences," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 988-1003, July.
    4. 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.
    5. Alexandra Lesnikowski & Ella Belfer & Emma Rodman & Julie Smith & Robbert Biesbroek & John D. Wilkerson & James D. Ford & Lea Berrang‐Ford, 2019. "Frontiers in data analytics for adaptation research: Topic modeling," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 10(3), May.
    6. Van Maanen, John (John Eastin), 1973. "Working the street; a developmental view of police behavior," Working papers 681-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    7. Margaret Roberts & Brandon Stewart & Tingley, Dustin, 2014. "stm: R Package for Structural Topic Models," Working Paper 176291, Harvard University OpenScholar.
    8. RePass, David E., 1929. "Issue Salience and Party Choice," American Political Science Review, Cambridge University Press, vol. 65(2), pages 389-400, June.
    9. Paoline, Eugene A., 2003. "Taking stock: Toward a richer understanding of police culture," Journal of Criminal Justice, Elsevier, vol. 31(3), pages 199-214.
    10. 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.
    11. 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. 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).
    3. 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).
    4. 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.
    5. Adams, Ian T., 2022. "Modeling Officer Perceptions of Body-worn Cameras: A National Survey," Thesis Commons fnxbg, Center for Open Science.
    6. Minwoo Ahn & Raksha Balakrishna & Michael Simeone & Marco A. Janssen, 2024. "When does group chat promote cooperation in shared resource governance?," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    7. 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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