Vehicle impoundments improve drinking and driving licence suspension outcomes: Large-scale evidence from Ontario
Bibliographic record
Abstract
Although vehicle impoundment has become a common sanction for various driving offences, large-scale evaluations of its effectiveness in preventing drinking and driving recidivism are almost non-existent in the peer-reviewed literature. One reason is that impoundment programs have typically been introduced simultaneously with other countermeasures, rendering it difficult to disentangle any observed effects. Previous studies of impoundment effectiveness conducted when such programs were implemented in isolation have typically been restricted to small jurisdictions, making high-quality evaluation difficult. In contrast, Ontario's "long-term" and "seven-day" impoundment programs were implemented in relative isolation, but with tight relationships to already existing drinking and driving suspensions. In this work, we used offence data produced by Ontario's population of over 9 million licensed drivers to perform interrupted time series analysis on drinking and driving recidivism and on rates of driving while suspended for drinking and driving. Our results demonstrate two key findings: (1) impoundment, or its threat, improves compliance with drinking and driving licence suspensions; and (2) addition of impoundment to suspension reduces drinking and driving recidivism, possibly through enhanced suspension compliance.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".