A diverting quarter century? Evidence-based police-led diversion 25 years on
Bibliographic record
Abstract
It is more than 25 years since Lawrence Sherman delivered his ‘Ideas in American Policing’ lecture on ‘evidence-based policing’ for the US Police Foundation. The original work [Sherman LW (1998) Evidence-Based Policing. Ideas in American Policing. Washington, DC: Police Foundation], Sherman’s equally seminal study on ‘preventing crime’ [Sherman LW, Gottfredson D, MacKenzie D, et al. (1997) Preventing Crime: What Works, What Doesn’t, What’s Promising. Washington, DC: Office of Justice Programs] and the subsequent development of evidence-based policing in the ‘Triple T’ [Sherman LW (2013) The rise of evidenced-based policing: targeting, testing and tracking. In: Tonry M (ed.) Crime and Justice in America 1975–2025. Chicago: University of Chicago Press] have been immensely influential as well as, for some, controversial. In 1998, Sherman challenged scholars, practitioners and policymakers to use the ‘best available evidence’ to guide policy and practice. He then posed four questions: ‘What is it? What is new about it? How does it apply to a specific example of police practice? How can it be institutionalized?’. Taking the lead from Sherman's questions, this contribution to the 25-year anniversary collection focuses on one example – police-led diversion – to illustrate the development and challenges of institutionalizing evidence-based policing since 1998.
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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 | 0.027 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".