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Record W7115704775 · doi:10.3366/legal.2025.0102

Colonial-Carceral Violence: The Pains of Imprisonment for First Nations Women

2025· article· en· W7115704775 on OpenAlexaboutno aff

Bibliographic record

VenueLegalities · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsImprisonmentPrisonColonialismDecolonizationSovereigntyEconomic JusticeStructural violence

Abstract

fetched live from OpenAlex

This article explores the structural violence that the colony inflicts on Aboriginal women in prisons. Based on two large-scale NSW studies, it highlights the impacts of imprisonment on Aboriginal women’s health, families and social, cultural and emotional wellbeing. The stories of Aboriginal women in prison are more than a glimpse into the ‘pains of imprisonment’. The walls, bars and cages do not define the totality of the pains imposed on Aboriginal women by the colonial regime. Aboriginal women suggest life beyond prison is endured as an open-air prison. This article argues that violence in prisons cannot be separated from the violence on the outside and that decolonisation is a necessary antidote. By engaging the voices of imprisoned First Nations women, the article shifts the focus in violence studies from inter-personal violence to state-sanctioned violence. It contends that strong community-controlled justice organisations that are run by and for Aboriginal women provide a pathway to strengthen and empower First Nations women. These organisations enact self-determination and sovereignty on the ground and counter the colonial structural violence of imprisonment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.020
Scholarly communication0.0040.003
Open science0.0010.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.311
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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