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Record W2951365028 · doi:10.22215/etd/2018-13299

The Effectiveness of Restorative Justice Programs: A Meta-Analysis of Recidivism and Other Outcomes

2018· dissertation· en· W2951365028 on OpenAlexafffund
Lindsay Fulham

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsCarleton University
FundersUniversity of TorontoStrong
KeywordsRecidivismRestorative justiceHarmAccountabilityPsychologyEconomic JusticeCriminal justiceCriminologyMeta-analysisApplied psychologyClinical psychologyPolitical scienceSocial psychologyMedicineLaw

Abstract

fetched live from OpenAlex

Restorative justice (RJ) is an alternative approach to the traditional criminal justice system (CJS) that focuses on repairing harm.Despite the recent proliferation of RJ programs, research suggests that their efficacy depends on various factors such as study methodology.The goal of the present study was to synthesize previous research on the effects of RJ in reducing recidivism as well as improving other outcomes.The findings from 59 studies on 67 samples examining the effectiveness of RJ programs were analyzed.The results revealed that RJ was associated with significant moderate reductions in general recidivism and improvements in satisfaction, procedural justice, offender accountability, offender attitudes, and reoffence severity.There were significant sample, study, and program moderators for general recidivism and victim procedural justice.Taken together, the results provide moderate support for the efficacy of RJ programs in reducing recidivism and suggest their potential for improving other outcomes over traditional CJS approaches.

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)0.022
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.052
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.033
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.090
GPT teacher head0.411
Teacher spread0.321 · 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 designMeta-analysis
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
Published2018
Admission routes2
Has abstractyes

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