Piotr Bystranowski (Interdisciplinary Centre for Ethics; Jagiellonian University) and Kevin Tobia
Georgetown University Law Center; Georgetown University – Department of Philosophy) have posted “Measuring Meta-Interpretation” (Journal of Institutional and Theoretical Economics (Forthcoming)) on SSRN. Here is the abstract:
American legal interpretation has taken an empirical turn. Courts and scholars use corpus linguistics, survey experiments, and machine learning to clarify legal texts’ meanings. We introduce these developments in “issue-level interpretation,” concerning interpretive theories’ application to legal language. Empirical methods also inform “meta-interpretative” debate: Which interpretive theory do interpreters use; which have they used; and which should they use? We demonstrate machine learning’s relevance to these meta-interpretive debates with insights provided by word embeddings that we trained on a corpus of over 1.3 million U.S. federal court decisions.