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The Impact of Legal Advocacy Experience Within the US Supreme Court on Trial Decision Outcomes

The Impact of Legal Advocacy Experience Within the US Supreme Court on Trial Decision Outcomes

Michael D'Rosario
Copyright: © 2017 |Volume: 8 |Issue: 1 |Pages: 12
ISSN: 1947-8569|EISSN: 1947-8577|EISBN13: 9781522512875|DOI: 10.4018/IJSDS.2017010105
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MLA

D'Rosario, Michael. "The Impact of Legal Advocacy Experience Within the US Supreme Court on Trial Decision Outcomes." IJSDS vol.8, no.1 2017: pp.65-76. http://doi.org/10.4018/IJSDS.2017010105

APA

D'Rosario, M. (2017). The Impact of Legal Advocacy Experience Within the US Supreme Court on Trial Decision Outcomes. International Journal of Strategic Decision Sciences (IJSDS), 8(1), 65-76. http://doi.org/10.4018/IJSDS.2017010105

Chicago

D'Rosario, Michael. "The Impact of Legal Advocacy Experience Within the US Supreme Court on Trial Decision Outcomes," International Journal of Strategic Decision Sciences (IJSDS) 8, no.1: 65-76. http://doi.org/10.4018/IJSDS.2017010105

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Abstract

Can advocacy experience differentials be used in formulating a model to predict trial outcomes in the US Supreme Court? In recent years, a number of studies have considered the role of experience before the Supreme Court in the determination of trial outcomes. The work of Sheehan, Mishler and Songer supports the assertion that trial experience possessed by trial lawyers is associated with disproportionate rates of success. McGuire is a significant study into the impact of the experience of competing trial lawyers on judicial decision making. The study identified the experience differentials of lawyers and sought to determine the impact of these differentials on trial outcomes. The study found that trial experience possessed by trial lawyers was associated with favourable trial outcomes. The current study extends upon McGuire, assessing the robustness of the original study employing a series of more advanced parametric estimation techniques. The study then uses the McGuire logistic model framework to develop a model of prediction, employing a backward propagation, multilayer perceptron network model.

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