JOURNAL ARTICLE

Now with the possibility of parole: Enabling a juvenile lifer's meaningful review.

  • Published In: British Journal of Criminology, 2023, v. 63, n. 1. P. 115 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kokkalera, Stuti S; Singer, Simon I 3 of 3

Abstract

This article examines how parole boards in one U.S. state justify granting or denying parole to juvenile lifers—individuals sentenced to life without parole (LWOP) for first-degree murder committed as adolescents—following Supreme Court rulings that mandate a meaningful opportunity for release based on "Miller factors." These factors include considerations of a juvenile’s immaturity, family environment, peer influence, participation in the crime, and rehabilitative potential. Analyzing 33 written parole decision statements from initial hearings between 2014 and 2018, the study finds that granting parole often hinges on narratives emphasizing childhood abuse, peer dependency, and demonstrated rehabilitation, while denials focus on the severity and “horrific” nature of the offense and question the candidate’s credibility or remorse. The findings highlight a selective and symbolic use of Miller factors by parole boards, where reduced culpability and rehabilitation are acknowledged but frequently outweighed by offense-based considerations, underscoring tensions between Supreme Court mandates and parole board discretion in discretionary release decisions for juvenile lifers.

Additional Information

  • Source:British Journal of Criminology. 2023/01, Vol. 63, Issue 1, p115
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2023
  • ISSN:0007-0955
  • DOI:10.1093/bjc/azac001
  • Accession Number:160901946
  • Copyright Statement:Copyright of British Journal of Criminology is the property of Oxford University Press / USA 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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