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Restorative Justice and Indigenous Courts Within the Penal Continuum: Rethinking Indigenous Over-Incarceration in Canada

2025· preprint· en· W4407776736 on OpenAlexaboutno aff
H. Wilke

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousRestorative justiceCriminologyEconomic JusticePolitical scienceLawCriminal justiceSociology

Abstract

fetched live from OpenAlex

Restorative justice has emerged as a comprehensive response to the over-incarceration of Indigenous peoples in Canada. Landmark developments – such as the 1999 Gladue decision and the creation of Indigenous People’s Courts (IPC) – have reshaped sentencing by integrating factors like discrimination and adverse socio-economic conditions. Beyond legal reform, restorative justice addresses colonial legacies and social inequalities. This article examines its role in recent Canadian initiatives – specifically the Royal Commission on Aboriginal Peoples and the Sentencing Reform Act (both enacted in 1996) – and draws on three months of fieldwork at the Indigenous People’s Court in Ottawa. The analysis focuses on the pivotal role of counseling and innovative restorative or communitarian programs within the IPC framework. Notably, many cases leading to trial do not stem from initial infractions but from failures to comply with conditional sentences under the Gladue principles – which emphasize diversion, probation, and “restoration” through counseling. The flexible notion of restoration – achieved by promoting resilience – facilitates ongoing behavioral profiling and supports emerging networks of experts. These mechanisms broaden the penal net by considering individual backgrounds more deeply and by expanding the range of interventions available that are not perceived as punitive sentences. It is proposed a nuanced perspective that views restorative justice and punitive measures as convergent, thereby revealing policy biases and contributing to the expansion of the penal system and its selectivity.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.197
Threshold uncertainty score0.931

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0480.015
Scholarly communication0.0080.002
Open science0.0040.008
Research integrity0.0030.006
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 head0.078
GPT teacher head0.357
Teacher spread0.279 · 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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