Oldfather et al. on Automated Content Analysis, Court Opinions, and Legal Scholarly Methodology

Professor Chad M. Oldfather of Marquette University School of Law, Professor Dr. Joseph P. Bockhorst of the University of Wisconsin Madison Department of Electrical Engineering and Computer Science, and Brian P. Dimmer, Esq., have published Triangulating Judicial Responsiveness: Automated Content Analysis, Judicial Opinions, and the Methodology of Legal Scholarship, Florida Law Review, 64, 1189-1242 (2012).

Here is the abstract:

The increasing availability of digital versions of court documents, coupled with increases in the power and sophistication of computational methods of textual analysis, promises to enable both the creation of new avenues of scholarly inquiry and the refinement of old ones. This Article advances that project in three respects. First, it examines the potential for automated content analysis to mitigate one of the methodological problems that afflicts both content analysis and traditional legal scholarship — their acceptance on faith of the proposition that judicial opinions accurately report information about the cases they resolve and courts’ decisional processes. Because automated methods can quickly process large amounts of text, they allow for assessment of the correspondence between opinions and other documents in the case, thereby providing a window into how closely opinions track the information provided by the litigants. Second, it explores one such novel measure — the responsiveness of opinions to briefs — in terms of its connection to both adjudicative theory and existing scholarship on the behavior of courts and judges. Finally, it reports our efforts to test the viability of automated methods for assessing responsiveness on a sample of briefs and opinions from the United States Court of Appeals for the First Circuit. Though we are focused primarily on validating our methodology, rather than on the results it generates, our initial investigation confirms that even basic approaches to automated content analysis provide useful information about responsiveness, and generates intriguing results that suggest avenues for further study.

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