The Impact of an Intensive Supervision Program on High-Risk Offenders: Manitoba’s COHROU Program
Bibliographic record
Abstract
Intensive supervision programs (ISP) have a long history in the United States but only a relatively short existence in Canada. Manitoba’s Criminal Organization High Risk Offender Unit (COHROU) program combines intensive supervision, support, and program placement with rapid police response in the event of non-compliance with supervision conditions. COHROU attempts to use evidence-based programs to address criminogenic needs and supplement surveillance. This quantitative retrospective study assesses program clients admitted over 8 years (N = 409), following up on new convictions for violent, property, breach of probation, and other offences both during the program and for a 2-year period following supervision. Days in custody are also tracked 3 years pre- and post-program. Findings indicate that reoffending was substantial but that a large number of convictions were technical violations. Using benchmark comparisons pre- and post-program, reductions were observed in violent reoffence, days in custody, and overall crime severity. While the downward trends in offence seriousness and incarceration are promising, the evaluation’s claims of ISP efficacy are limited by the lack of a comparison group. Future researchers may also wish to investigate more qualitative aspects of COHROU program operation and probation officer supervision to understand what features of the program affect participants.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".