IDEAS home Printed from https://ideas.repec.org/a/cup/apsrev/v51y1957i01p1-12_07.html
   My bibliography  Save this article

Predicting Supreme Court Decisions Mathematically: A Quantitative Analysis of the “Right to Counsel†Cases

Author

Listed:
  • Kort, Fred

Abstract

This study represents an attempt to apply quantitative methods to the prediction of human events that generally have been regarded as highly uncertain, namely, decisions by the Supreme Court of the United States. The study is designed to demonstrate that, in at least one area of judicial review, it is possible to take some decided cases, to identify factual elements that influenced the decisions, to derive numerical values for these elements by using a formula, and then to predict correctly the decisions of the remaining cases in the area specified. The analysis will be made independently of what the Court said by way of reasoning in these cases; it will rely only on the factual elements which have been emphasized by the justices in their opinions and on their votes to affirm or set aside convictions. Changes in Court personnel made no decisive difference in the pattern of judicial action in this area; so the analysis will not need to take into account the fact that twenty-five different justices have occupied the nine seats on the Court during the period covered, i.e., the past quarter century.

Suggested Citation

  • Kort, Fred, 1957. "Predicting Supreme Court Decisions Mathematically: A Quantitative Analysis of the “Right to Counsel†Cases," American Political Science Review, Cambridge University Press, vol. 51(1), pages 1-12, March.
  • Handle: RePEc:cup:apsrev:v:51:y:1957:i:01:p:1-12_07
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0003055400070659/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jonathan P. Kastellec & Jeffrey R. Lax, 2008. "Case Selection and the Study of Judicial Politics," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 5(3), pages 407-446, September.
    2. Junyi Chen & Xuanqing Zhang & Xiabing Zhou & Yingjie Han & Qinglei Zhou, 2023. "An Approach Based on Cross-Attention Mechanism and Label-Enhancement Algorithm for Legal Judgment Prediction," Mathematics, MDPI, vol. 11(9), pages 1-19, April.
    3. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:apsrev:v:51:y:1957:i:01:p:1-12_07. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/psr .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.