The Need for Comprehensive Crime Prevention Planning: The Case of Motor Vehicle Theft
Bibliographic record
Abstract
Most crime prevention programs are poorly planned and implemented, and therefore do little or nothing to prevent crime. Programs are typically fragmented, with little communication among the groups who share a common interest in reducing crime. Most programs operate in isolation rather than being linked to a broader community-wide prevention strategy. Communities that have been able to make meaningful reductions in crime rates have done so by taking a comprehensive approach to crime prevention in which they implement an integrated series of programs that coordinate the efforts of a broad range of partners and participants. To be comprehensive and effective, crime prevention programs must analyse the crime problems in their community context, involve a broad group of people and organizations, consider a diverse range of prevention strategies, carefully implement the programs most suited to a particular community, and assess the results. This article illustrates how this process can be applied to the prevention of motor vehicle theft.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".