As the most public component of the Supreme Court's decision-making process, oral argument receives an out-sized share of attention in the popular media. Despite its prominence, however, the basic function and operation of oral argument as an institution remains poorly understood, as political scientists and legal scholars continue to debate even the most fundamental questions about its role. Past study of oral argument has tended to focus on discrete, quantifiable attributes of oral argument, such as the number of questions asked to each advocate, the party of the Justices' appointing president, or the ideological implications of the case on appeal. Such studies allow broad generalizations about oral argument and judicial decision making: Justices tend to vote in accordance with their ideological preferences, and they tend to ask more questions when they are skeptical of a party's position. But they tell us little about the actual goings on at oral argument -- the running dialog between Justice and advocate that is the heart of the institution. This Article fills that void, using machine learning techniques to, for the first time, construct predictive models of judicial decision making based not on oral argument's superficial features or on factors external to oral argument, such as where the case falls on a liberal-conservative spectrum, but on the actual content of the oral argument itself -- the Justices' questions to each side. The resultant models offer an important new window into aspects of oral argument that have long resisted empirical study, including the Justices' individual questioning styles, how each expresses skepticism, and which of the Justices' questions are most central to oral argument dialog.

翻译：作为最高法院决策过程中最公开的环节，口头辩论在主流媒体中获得了极大的关注。然而，尽管其重要性显著，但口头辩论作为一项制度的基本功能和运作方式仍鲜为人知——政治学家和法律学者甚至对其最基本的作用问题仍争论不休。以往对口头辩论的研究往往聚焦于离散、可量化的特征，例如向每位辩护律师提出的问题数量、任命大法官的总统所属党派，或上诉案件的意识形态倾向。这类研究得出了关于口头辩论与司法决策的宏观结论：大法官倾向于依据其意识形态偏好投票，且对某一方立场持怀疑态度时会提出更多问题。但这些研究几乎未揭示口头辩论的真实过程——即作为该制度核心的大法官与辩护律师之间的动态对话。本文填补了这一空白，首次运用机器学习技术，基于口头辩论本身的实质内容（即大法官对双方提出的问题），而非其表面特征或外部因素（如案件在自由-保守光谱中的位置），构建了司法决策的预测模型。这些模型为长期难以进行实证研究的口头辩论维度提供了重要新视角，包括大法官各自的提问风格、表达质疑的方式，以及哪些问题在口头辩论对话中最为关键。