The effectiveness of probation supervision towards reducing reoffending: A Rapid Evidence Assessment
Bibliographic record
Abstract
In response to the lack of universal agreement about ‘What Works’ in probation supervision (Trotter, 2013) we undertook a Rapid Evidence Assessment of the empirical literature. Our analysis of research into the effect of probation supervision reducing reoffending included 13 studies, all of which employed robust research designs, originating in the USA, UK, Canada and Australia, published between 2006 and 2016. We describe the papers included in our review, and the meta-analyses of their findings. Overall, we found that the likelihood of reoffending was shown to be lower for offenders who had been exposed to some type of supervision. This finding should be interpreted cautiously however, given the heterogeneity of the studies. We suggest future research and methodological considerations to develop the evidence base concerning the effectiveness of probation supervision.
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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 | 0.164 | 0.401 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".