Preventing the Theft of Motor Vehicles: The Limits of Deterrence
Bibliographic record
Abstract
<div class="htmlview paragraph">This article draws on in-depth qualitative interviews conducted with young auto thieves in Eastern Ontario as part of AUTO21's National Study of Young Offender Involvement in Motor Vehicle Theft. To help develop possible avenues for the prevention of car theft, this paper focuses on the techniques utilized to steal motor vehicles and on how young people understand their participation in auto theft. It was apparent from the young people's responses that the reduction of auto theft would not be accomplished by focusing exclusively on improving the security features of motor vehicles. For some young people, security devices held little deterrent value and could be easily circumvented. However, certain security devices (e.g. GPS tracking) did hold some potential deterrent value for specific vehicles (e.g. high-end vehicles). Unfortunately, this meant that young people simply moved on to more susceptible vehicles. Threat of punishment and a variety of situational crime prevention techniques (e.g. parking lot attendants, bright lighting) also held little deterrent value.</div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".