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Doctrine as Data

Alan Gaitenby, University of Massachusetts Amherst

Abstract

Doctrine as Data examines the issues and opportunities around machine acquisition and analysis of legal doctrine. This work sought to treat doctrine as data, as a clump of federal appellate case opinion texts, which could be procured and empirically analyzed with information processing technology. Doctrine is a nimble knowledge structure however, existing as a clump as well as a logic where parameters and understandings in case law are constituted. The subject doctrine for this project, compelling interests of the strict scrutiny balancing test, proved to be a logic where notions of legitimate police power and individual rights are established. That logic is flexible, politically sensitive, and responsive, going beyond opinions from a myriad of cases said manifesting doctrine. Doctrine as Data examines information systems and their practices of indexing and accessing appellate case opinions to explore whether these systems are significant to sustaining, or challenging, conceptualizations of doctrine in cases. The examination consists of defining, identifying, and collecting appellate case opinions exhibiting the compelling interest doctrine using the preeminent hard bound and computer legal information systems (i.e. West's digests and reporters and Lexis/Nexis respectively). The project also introduces a new tool, the InQuery search engine from the University of Massachusetts' Center for Intelligent Information Retrieval, to analyze that collection for conceptual coherency attributed to doctrine, i.e. to probe doctrine's presence in, and relationship to, case opinions. It appears however that doctrine exists outside of cases, or rather, is attributed to cases through traditions of legal practice, commentary, and scholarship moreso than in the systems created to manage law's hard data.

Subject Area

Law|Information science

Recommended Citation

Gaitenby, Alan, "Doctrine as Data" (2000). Doctoral Dissertations Available from Proquest. AAI9960752.
https://scholarworks.umass.edu/dissertations/AAI9960752

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