Evidence-Based Crime Prevention: Conclusions and Directions for a Safer Society
Bibliographic record
Abstract
In an evidence-based society, government crime prevention policy and local practice would be based on interventions with demonstrated effectiveness in preventing crime - using what works best. Systematic reviews are the most comprehensive method of assessing the effectiveness of crime prevention measures and, in an evidence-based society, they would be the source that governments would turn to for help in the development of policy. This article summarizes the main findings of a project of the Campbell Collaboration Crime and Justice Group to advance knowledge on what works to prevent crime for a wide range of interventions, organized around four important domains: at-risk children, offenders, victims, and high-crime places. The full conclusions are published in the forthcoming book, Preventing Crime: What Works for Children, Offenders, Victims, and Places. The good news from this first wave of reviews is that most of the interventions are effective in preventing crime and, in many cases, produce sizeable effects. This includes social-skills training for children, cognitive-behavioural therapy and incarceration-based drug treatment for offenders, face-to-face restorative justice conferences involving victims and offenders, prevention of repeat residential burglary victimization, hot spots policing, closed-circuit television surveillance, and improved street lighting. Acting on the evidence from these systematic reviews could contribute to a safer society, both now and in the long run. Alongside the Campbell Collaboration effort to prepare and maintain systematic reviews for use by policy makers, practitioners, and the general public, a program of research into new crime prevention and intervention experiments needs to be initiated.
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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 | 0.169 | 0.190 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".