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Docket Control, Mandatory Jurisdiction, And The Supreme Court's Failure In Rucho V. Common Cause, Carolyn Shapiro
Docket Control, Mandatory Jurisdiction, And The Supreme Court's Failure In Rucho V. Common Cause, Carolyn Shapiro
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This paper, part of a Symposium on Andrew Coan's book, Rationing the Constitution: How Judicial Capacity Shapes Supreme Court Decision-Making, traces congressional changes to Supreme Court jurisdiction over more than a century, noting that those changes were regularly made in response to concerns about the Court's caseload. To the extent that Coan, and the Court, turn to doctrinal methods of controlling caseloads, such as deferential standards of review, they are overlooking the important congressional role in setting the Court's jurisdiction. The paper concludes by criticizing the recent decision of Rucho v. Common Cause in which the Court held that extreme …
Foster V. Chatman: A Missed Opportunity For Batson And The Peremptory Challenge, Nancy Marder
Foster V. Chatman: A Missed Opportunity For Batson And The Peremptory Challenge, Nancy Marder
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In 2016, the United States Supreme Court decided that the prosecutors in Foster v. Chatman exercised race-based peremptory challenges in violation of Batson v. Kentucky. The Court reached the right result, but missed an important opportunity. The Court should have acknowledged that after thirty years of the Batson experiment, it is clear that Batson is unable to stop discriminatory peremptory challenges. Batson is easy to evade, so discriminatory peremptory challenges persist and the harms from them are significant. The Court could try to strengthen Batson in an effort to make it more effective, but in the end the only way …
A General Approach For Predicting The Behavior Of The Supreme Court Of The United States, Daniel Katz
A General Approach For Predicting The Behavior Of The Supreme Court Of The United States, Daniel Katz
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Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve …