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How to Lose Cases and Influence People

Author

Listed:
  • Hinkle Rachael K.

    (Department of Political Science, University at Buffalo, SUNY, 520 Park Hall, Buffalo, NY, USA)

  • Nelson Michael J.

    (Department of Political Science, The Pennsylvania State University, University Park, PA, USA)

Abstract

Dissenting opinions are common in the US Supreme Court even though they take time and effort, risk infuriating colleagues, and have no precedential value. In spite of these drawbacks, dissents can potentially contribute to future legal development. We theorize that dissenting justices who use more memorable language are more successful in achieving such long-term impact. To test this theory, we amass an original dataset of citations to dissenting opinions extracted directly from majority opinion text. We further leverage these texts to build an algorithm that quantifies the distinctiveness of dissenting language within a dynamic context. Our results indicate that dissents using more negative emotion and more distinctive words are cited more in future majority opinions. These results contribute to our understanding of how language can influence long-term policy development.

Suggested Citation

  • Hinkle Rachael K. & Nelson Michael J., 2017. "How to Lose Cases and Influence People," Statistics, Politics and Policy, De Gruyter, vol. 8(2), pages 195-221, December.
    • Handle: RePEc:bpj:statpp:v:8:y:2017:i:2:p:195-221:n:5
    DOI: 10.1515/spp-2017-0013
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    References listed on IDEAS

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    1. Martin, Andrew D. & Quinn, Kevin M., 2002. "Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999," Political Analysis, Cambridge University Press, vol. 10(2), pages 134-153, April.
    2. Ryan C. Black & James F. Spriggs, 2013. "The Citation and Depreciation of U.S. Supreme Court Precedent," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 10(2), pages 325-358, June.
    3. Carter, David B. & Signorino, Curtis S., 2010. "Back to the Future: Modeling Time Dependence in Binary Data," Political Analysis, Cambridge University Press, vol. 18(3), pages 271-292, July.
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