JOURNAL ARTICLE
Lexical Complexity in US Statutes.
Published In: International Journal of Speech, Language & the Law, 2025, v. 32, n. 1. P. 34 1 of 3
Database: Communication Source 2 of 3
Authored By: Hashimoto, Brett; Brown, Earl Kjar; Marshall, Catherine 3 of 3
Abstract
This article examines the lexical complexity of U.S. federal statutory language, specifically the United States Code (US Code), by comparing it to three registers of American English: television/movie subtitles, news articles, and academic journal articles. The study finds that statutory language is characterized by higher lexical sophistication and nominal density but lower lexical diversity and content word density than the other registers. These features reflect the frequent use of rare, abstract, and technical terms, as well as repeated precise terminology and extensive nominal phrase modification, which contribute to the linguistic complexity and potential difficulty in comprehension for lay readers. The authors suggest that statutory language might be simplified by employing more common words and increasing clausal (rather than phrasal) modification to enhance clarity without sacrificing legal precision. The findings have implications for legislative drafters, legal linguists, and educators aiming to improve accessibility of legal texts, while acknowledging the need for further research on other legal registers and the impact of simplification on legal efficacy.
Additional Information
- Source:International Journal of Speech, Language & the Law. 2025/01, Vol. 32, Issue 1, p34
- Document Type:Article
- Subject Area:Political Science
- Publication Date:2025
- ISSN:1748-8885
- DOI:10.3138/ijsll-2024-0023
- Accession Number:190406016
- Copyright Statement:Copyright of International Journal of Speech, Language & the Law is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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