The effectiveness of vehicle security devices and their role in the crime drop
Bibliographic record
Abstract
Car theft in the UK fell two-thirds from the mid-1990s as part of more widespread crime drops, and has been attributed to improved vehicle security. This study develops a Security Impact Assessment Tool (SIAT) to gauge the contribution of individual security devices and their combination. The metric of impact derived is termed the Security Protection Factor (SPF). Cars with central locking plus an electronic immobilizer, and often an alarm, are found to be ‘SPF 25’, that is, they were up to 25 times less likely to be stolen than those without security. That impact is greater than expected from the individual contributions of those devices, and is attributed to interaction effects. Tracking devices are found to be particularly effective but rarer. Protective effects were greater against theft of cars than against theft from cars or attempts, almost certainly reflecting the difficulty imposed on thieves by electronic immobilizers. It is suggested that this type of analysis could be usefully extended to other crime types and security combinations. The analysis also lends support to a ‘security hypothesis’ component of an explanation for the major national and international crime drops that is based in the criminologies of everyday life.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".