Police and Crown Prosecutor Use of Restorative Justice and Diversion for Adults and Youth in a High-Crime Area
Bibliographic record
Abstract
This research assesses the use of diversion and restorative justice (RJ) referrals by patrol officers and Crown prosecutors in a high-crime urban community. Of interest was the influence on referral of legal factors such as prior criminal history and offence severity and the potential impact of extra-legal factors such as age, gender, and Indigenous ethnicity. Data on 1,000 eligible offenders during a 6-month study period were analyzed. About 15% of eligible cases were referred. Police and Crown were not as aggressive as desirable, with the police passing on some eligible low-severity cases later referred by prosecutors. As prior criminal history increased referrals declined, with a more pronounced impact at the patrol officer level. In bivariate analysis, age, gender, and marital, student, and unemployment status exerted small effects, but these were non-significant in multivariate analysis. No evidence of ethnic disparity was found. Crime types of less serious violence and lower property, as well as prior convictions, had moderate bivariate effects, and these persisted in multivariate analysis. Although most cases referred had a limited criminal history and involved minor crimes, police and prosecutors did refer a few involving serious violent offences and prior convictions, showing potential for greater use of RJ.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".