JOURNAL ARTICLE

Investigating the relationship between L2 proficiency and Miranda rights comprehension: a partial replication of Pavlenko et al. (2019).

  • Published In: International Journal of Speech, Language & the Law, 2024, v. 31, n. 2. P. 291 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Benzaia, Leigh Anne; Jarvis, Scott; Akbary, Mary; Park, Hae In 3 of 3

Abstract

This article focuses on replicating and extending research on the comprehension of the Miranda warning by non-native (L2) English speakers, using an elicited imitation (EI) test embedded with Miranda sentences to assess both English proficiency and Miranda comprehension (MC). The study partially replicates Pavlenko et al. (2019) with a different L2 population—immigrants to the US—and a different method, confirming that L2 speakers generally struggle to comprehend the Miranda warning, though a somewhat higher proportion (19.4%) met the comprehension threshold compared to the original study (2.8%). Results show a strong correlation between EI scores (English proficiency) and MC scores, and a logistic regression model demonstrated that EI scores can reliably predict whether an individual reaches the MC threshold, providing probabilistic data useful for forensic linguistics and legal contexts. The study argues that EI testing is preferable to paraphrasing tasks for assessing comprehension deficits in L2 speakers and highlights the need for further research on functional understanding of Miranda rights beyond linguistic comprehension.

Additional Information

  • Source:International Journal of Speech, Language & the Law. 2024/07, Vol. 31, Issue 2, p291
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2024
  • ISSN:1748-8885
  • DOI:10.3138/ijsll-2024-0018
  • Accession Number:184271699
  • 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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