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Record W2317254883 · doi:10.1177/0021934714526042

Bringing Race Relations Into the Restorative Justice Debate

2014· article· en· W2317254883 on OpenAlexafffund
Theo Gavrielidés

Bibliographic record

VenueJournal of Black Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsSimon Fraser University
FundersEuropean CommissionSimon Fraser UniversityBucks New University
KeywordsRestorative justiceCriminologyCriminal justiceContext (archaeology)Economic JusticeTheory of criminal justiceDemiseRetributive justiceRace (biology)Extant taxonSociologyExpansivePolitical scienceLawGender studiesHistory

Abstract

fetched live from OpenAlex

Restorative justice was reborn in the 1970s with a promise to provide a better sense and experience of justice, especially for those who are let down the most by the criminal justice system. And yet, despite well-evidenced disproportionality and race inequality issues within criminal justice institutions, restorative justice research and practice within the context of race are almost nonexistent. This article aims to unravel this paradox while looking at the scant extant literature to explore the alternative and more personalized restorative justice vision of “the other” and cultural differences. An expansive conceptual model that is aligned with the integrative nature of restorative justice is then posited for further pilots and research. The article warns that if restorative justice continues to ignore the challenges raised within a race equality context, the power structures inherent within our current structural framework of criminal justice will lead to its demise.

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)0.014
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0270.058
Scholarly communication0.0160.021
Open science0.0020.013
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0060.000

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 head0.043
GPT teacher head0.371
Teacher spread0.327 · 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 designTheoretical or conceptual
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

Citations55
Published2014
Admission routes2
Has abstractyes

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