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Record W3136271386 · doi:10.5553/tijrj.000077

Risk, restorative justice and the Crown

2021· article· en· W3136271386 on OpenAlexaboutno aff
Brendyn Johnson

Bibliographic record

VenueThe International Journal of Restorative Justice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsCrown (dentistry)Restorative justiceCriminologyPsychologyPolitical scienceDentistryMedicine

Abstract

fetched live from OpenAlex

Risk, restorative justice and the Crown a study of the prosecutor and institutionalisation in Canada In Canada, restorative justice programmes have long been institutionalised in the criminal justice system. In Ontario, specifically, their use in criminal prosecutions is subject to the approval of Crown attorneys (prosecutors) who are motivated in part by risk logics and risk management. Such reliance on state support has been criticised for the ways in which it might subvert the goals of restorative justice. However, neither the functioning of these programmes nor those who refer cases to them have been subject to much empirical study in Canada. Thus, this study asks whether Crown attorneys’ concerns for risk and its management impact their decision to refer cases to restorative justice programmes and with what consequences. Through in-depth interviews with prosecutors in Ontario, I demonstrate how they predicate the use of restorative justice on its ability to reduce the risk of recidivism to the detriment of victims’ needs. The findings suggest that restorative justice becomes a tool for risk management when prosecutors are responsible for case referrals. They also suggest that Crown attorneys bear some responsibility for the dangers of institutionalisation. This work thus contributes to a greater understanding of the functioning of institutionalised restorative justice in Canada.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.605
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.341
Teacher spread0.316 · 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.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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