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Machine learning analysis of a national sample of U.S. case law involving mental health evidence

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  • Thomaidou, Mia A.
  • Patel, Alisha
  • Xie, Sandy S.
  • Berryessa, Colleen M.

Abstract

Sentencing practices in cases involving defendants with mental disorders are often opaque, as data on case facts and sentencing decisions are not easily accessible.

Suggested Citation

  • Thomaidou, Mia A. & Patel, Alisha & Xie, Sandy S. & Berryessa, Colleen M., 2024. "Machine learning analysis of a national sample of U.S. case law involving mental health evidence," Journal of Criminal Justice, Elsevier, vol. 94(C).
    • Handle: RePEc:eee:jcjust:v:94:y:2024:i:c:s0047235224001156
    DOI: 10.1016/j.jcrimjus.2024.102266
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    References listed on IDEAS

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    1. Anderson, James M & Kling, Jeffrey R & Stith, Kate, 1999. "Measuring Interjedge Sentencing Disparity: Before and After the Federal Sentencing Guidelines," Journal of Law and Economics, University of Chicago Press, vol. 42(1), pages 271-307, April.
    2. Jonathan P. Kastellec, 2010. "The Statistical Analysis of Judicial Decisions and Legal Rules with Classification Trees," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 7(2), pages 202-230, June.
    3. Adam Bonica & Maya Sen, 2021. "Estimating Judicial Ideology," Journal of Economic Perspectives, American Economic Association, vol. 35(1), pages 97-118, Winter.
    4. Alma Cohen & Crystal S. Yang, 2019. "Judicial Politics and Sentencing Decisions," American Economic Journal: Economic Policy, American Economic Association, vol. 11(1), pages 160-191, February.
    5. Joshua B. Fischman & Max M. Schanzenbach, 2012. "Racial Disparities Under the Federal Sentencing Guidelines: The Role of Judicial Discretion and Mandatory Minimums," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 9(4), pages 729-764, December.
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