JOURNAL ARTICLE

DNA Databanks as a Source of Information about the Criminal Behavior of Individuals Who Have Been Linked to Crimes but Not Identified by Police.

  • Published In: Canadian Journal of Criminology & Criminal Justice, 2024, v. 66, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Lavergne, Leo; Boivin, Rémi; Baechler, Simon; Séguin, Diane; Lefebvre, Jean-François; Fiola, Karine; Milot, Emmanuel 3 of 3

Abstract

This article focuses on comparing the criminal activities of identified and unidentified offenders using 19 years of forensic DNA match data from Quebec, Canada, provided by the Laboratoire de sciences judiciaires et de médecine légale (LSJML). The study finds that unidentified individuals—those whose DNA is found at crime scenes but who remain absent from police files—tend to commit fewer and less violent crimes, act more often alone, and specialize more in certain crime types, particularly secondary offenses such as drug and firearm possession. These patterns align with the exposure hypothesis, which suggests that offenders who commit less serious or solo crimes have a lower risk of detection, and the competence hypothesis, which posits that more skilled offenders evade arrest. Social network analysis reveals that unidentified offenders occupy strategic positions in co-offending networks, indicating their potential importance in criminal investigations and forensic intelligence. The study highlights the value of incorporating DNA data on unidentified offenders to enhance criminological research and policing strategies.

Additional Information

  • Source:Canadian Journal of Criminology & Criminal Justice. 2024/01, Vol. 66, Issue 1, p1
  • Document Type:Article
  • Subject Area:Religion and Philosophy
  • Publication Date:2024
  • ISSN:1707-7753
  • DOI:10.3138/cjccj-2022-0049
  • Accession Number:178888784
  • Copyright Statement:Copyright of Canadian Journal of Criminology & Criminal Justice 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.