Are Police-Led Social Crime Prevention Initiatives Effective? A Process and Outcome Evaluation of a UK Youth Intervention
Bibliographic record
Abstract
Police-led interventions with “at-risk” young people raise a number of debates around policing in society including the allocation of resources at a time of fiscal austerity, the extent to which the police should prioritize the safety and well-being of young people, and the role that the police should take in preventing youth crime. This article explores the impact and effectiveness of a police-led social crime prevention initiative in England. It adopts the QUALIPREV approach by Rummens, Hardyns,Vander Laenen, and Pauwels on behalf of the European Crime Prevention Network to analyze the data allowing for a detailed and replicable analysis of core aspects including police engagement, risk management, offending rates, and police–community relations. Drawing on comparisons between the UK case study and previous studies on police-led social crime prevention projects in Australia and Canada, this article identifies a number of common challenges for schemes of this nature including problems with multiagency working, developing a clear project identity, unequal resources across different locations, and the difficulty in recruiting and retaining volunteers. However, there were also significant benefits to such schemes, including positive impacts on offending rates, engagement of at-risk young people, and wider benefits to the communities within which the young people live, including participation, volunteering, and reduction in risks of community harm. A cost–benefit analysis also shows such schemes have the potential to offer significant savings to the criminal justice system as a whole.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.001 | 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 teacher head, 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".