Do Flood Mitigation and Natural Habitat Protection Employment Reduce Youth Offending?
Bibliographic record
Abstract
The present study examines the association between employment and offending for a sample of young offenders who are paid to work in a pilot programme known as the Skill Mill. First, we analyse a sample of 39 youths over a period of 10 years (40 quarters) to determine whether Skill Mill employed youth are more likely to desist from offending than a control group of youth who are not employed in the Skill Mill. Those youths employed by the Skill Mill committed 1.12 fewer offences per quarter than the control group ( p < 0.001). In addition, offending rates among the Skill Mill youths decreased by 0.99 offences per quarter after they began work ( p < 0.001). Next, we review results from semi-structured interviews with current Skill Mill employees and their supervisor that helps to unpack why the Skill Mill has been successful in promoting desistance. We conclude that programmes like the Skill Mill can mark an important turning point, and more specifically, a hook for change in the lives of young offenders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".