JOURNAL ARTICLE

Aftermath of a Storm: Women and Reparations for Residential School Abuses.

  • Published In: Canadian Journal of Women & the Law, 2024, v. 35, n. 2. P. 235 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hanson, Cindy; Farmer, Joanne; Hughes, Judy 3 of 3

Abstract

This article examines a gender-specific, community-based research project conducted in partnership with an Indigenous women's organization that analyzed the Independent Assessment Process (IAP), a key component of the Indian Residential Schools Settlement Agreement (IRSSA) designed to compensate Survivors of serious physical and sexual abuse in Canadian Indian residential schools (IRS). The study highlights how the IAP’s legal framework, lacking a gender and cultural lens, often failed to recognize the gendered and racialized experiences of Indigenous women, particularly by undervaluing unpaid caregiving work and privileging heteronormative definitions of sexual abuse. Drawing on testimonies from women Survivors, adjudicators, and support workers, the research underscores the need for reparations models that incorporate intersectional, culturally relevant approaches to better address colonial legacies, power imbalances, and the intergenerational trauma caused by the IRS system. The article concludes that future compensation and reconciliation efforts should integrate anti-racist, anti-sexist, and non-heteronormative perspectives to more effectively support healing and social justice for Indigenous communities.

Additional Information

  • Source:Canadian Journal of Women & the Law. 2024/11, Vol. 35, Issue 2, p235
  • Document Type:Article
  • Subject Area:Education
  • Publication Date:2024
  • ISSN:0832-8781
  • DOI:10.3138/cjwl.35.02.01
  • Accession Number:180807629
  • Copyright Statement:Copyright of Canadian Journal of Women & 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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