The effectiveness of vehicle security devices and their role in the crime drop
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it