JOURNAL ARTICLE

'A shifting precipice of unsettled law'? A survey of how US courts treat expert testimony using forensic stylistics.

  • Published In: International Journal of Speech, Language & the Law, 2023, v. 30, n. 1. P. 119 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Dundon, John Terry 3 of 3

Abstract

This article surveys how U.S. courts have treated expert testimony based on forensic stylistics, a method of authorship attribution analysis that compares stylistic features of questioned and known writings. It finds that while forensic stylistics testimony is generally admissible to identify similarities and differences between texts, courts consistently exclude expert opinions on ultimate authorship due to concerns about the method’s scientific reliability and lack of established error rates. Key federal cases such as United States v. Van Wyk (2000) and United States v. Zajac (2010) exemplify this approach, which contrasts with earlier rulings that left jurors to make authorship determinations without expert guidance. The article also notes that forensic stylistics is often treated analogously to handwriting analysis—accepted for technical comparison but not for definitive authorship conclusions—and that evidentiary standards vary across jurisdictions, with some state courts applying older rules like Frye. Overall, the study highlights a stable legal trend limiting forensic stylistics experts to descriptive testimony, reflecting ongoing debates about the method’s reliability and suggesting cautious use by practitioners and litigants.

Additional Information

  • Source:International Journal of Speech, Language & the Law. 2023/01, Vol. 30, Issue 1, p119
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2023
  • ISSN:1748-8885
  • DOI:10.1558/ijsll.23788
  • Accession Number:172801676
  • 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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