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Record W2117693048 · doi:10.1177/1557085108317551

Falling Between the Cracks of Retributive and Restorative Justice

2008· article· en· W2117693048 on OpenAlexaffabout
Gillian Balfour

Bibliographic record

VenueFeminist Criminology · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsTrent University
Fundersnot available
KeywordsCriminalizationRestorative justiceImprisonmentCriminologySociologyRetributive justiceContext (archaeology)LawEconomic JusticePolitical scienceHistory

Abstract

fetched live from OpenAlex

In 1996, the Canadian government introduced progressive sentencing law reforms that called for special consideration of the conditions in Aboriginal communities as legacies of colonialism and to limit the use of incarceration. At the same time, feminist-inspired law reforms sought compulsory criminalization and vigorous prosecution of gendered violence. Since that time, there has been a doubling of the rate of imprisonment of Aboriginal women, and gendered violence is three and a half times greater in Aboriginal communities. Using the sentencing decisions of two cases involving Aboriginal women convicted of manslaughter, the author explores the practice of law as a site of backlash and an appropriation of feminist-inspired antiviolence strategies. The author draws on feminist and critical race studies of restorative justice in the context of gendered violence to examine why the victimization–criminalization continuum has not been fully recognized in the practice of restorative justice.

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)0.015
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.398
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0290.179
Scholarly communication0.0210.012
Open science0.0040.014
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0030.000

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 head0.128
GPT teacher head0.356
Teacher spread0.229 · 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

Citations33
Published2008
Admission routes2
Has abstractyes

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