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Record W4295632964 · doi:10.1086/720943

Indigenizing Prisons: A Canadian Case Study

2022· article· en· W4295632964 on OpenAlexaboutno aff
Justin Everett Cobain Tetrault

Bibliographic record

VenueCrime and Justice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGenocidePrisonDignitySpiritualitySociologyColonialismCriminologyPolitical sciencePsychologyLawMedicine

Abstract

fetched live from OpenAlex

Mass incarceration of Indigenous peoples is a fundamental Canadian human rights problem. One response since the 1970s has been to “Indigenize” prisons by teaching Indigenous culture and history, facilitating spirituality, involving Elders and communities in rehabilitation, and creating special prisons called “healing lodges.” Criminologist proponents of “critical prison studies” are widely dismissive of these programs, with some arguing that Indigenized programming advances cultural genocide. They are wrong. University of Alberta Prison Project researchers interviewed nearly 600 prisoners in six prisons across western Canada, of whom 40 percent self-identified as Indigenous. Respondents generally praised Indigenizing initiatives for teaching them about their history and culture and helping them feel empowered and proud of their Indigenous identity. They said the initiatives helped them feel better able to cope with colonial traumas, including residential school and foster care system experiences; created a support network between Elders and fellow prisoners; and facilitated basic religious accommodation. Respondents’ criticisms focused on prison management, particularly security restrictions and staff prejudice that can prevent access to Indigenized resources. Indigenized programming supports the dignity and religious rights of incarcerated Indigenous peoples. Participants wanted expanded, more easily accessible cultural programming.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0240.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.339
Teacher spread0.300 · 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 teacher head, not a consensus.

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

Citations14
Published2022
Admission routes1
Has abstractyes

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