Measuring Meta-Interpretation

Piotr Bystranowski and Kevin Tobia

Journal of Institutional and Theoretical Economics (JITE), 2024, vol. 180, issue 2, 281-305

Abstract: American legal interpretation has taken an empirical turn. Courts and scholars use corpus linguistics, survey experiments, and machine learning to clarify meanings of legal texts. We introduce these developments in "issue-level interpretation," concerning interpretive theories' application to legal language. Empirical methods also inform "meta-interpretive" debate: Which interpretive theory do interpreters use; which have they used; and which should they use? We demonstrate the relevance of machine learning to these meta-interpretive debates with insights provided by a word embedding that we trained on a corpus of over 1.3 million U.S. federal court decisions.

Keywords: interpretation; meaning; natural language processing; purpose; text; word embeddings (search for similar items in EconPapers)
JEL-codes: K10 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mohrsiebeck.com/en/article/measuring-m ... 101628jite-2024-0011
Fulltext access is included for subscribers to the printed version.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mhr:jinste:urn:doi:10.1628/jite-2024-0011

Ordering information: This journal article can be ordered from
Mohr Siebeck GmbH & Co. KG, P.O.Box 2040, 72010 Tübingen, Germany

DOI: 10.1628/jite-2024-0011

Access Statistics for this article

Journal of Institutional and Theoretical Economics (JITE) is currently edited by Gerd Mühlheußer and Bayer, Ralph-C

More articles in Journal of Institutional and Theoretical Economics (JITE) from Mohr Siebeck, Tübingen
Bibliographic data for series maintained by Thomas Wolpert ().