THE IMPACT OF THE ADMINISTRATIVE DRIVER'S LICENCE SUSPENSION LAW IN ONTARIO
Bibliographic record
Abstract
Ontario introduced an Administrative Driver's Licence Suspension Law (ADLS) on November 29, 1996. This study aimed to evaluate public awareness of the law, it's effects on drinking-driving behaviour, and it's impact on alcohol-related fatal collisions. Knowledge and behaviour data were obtained from the Ontario Drug Monitor, a monthly cross-sectional general population survey of Ontario adults, collected during 1996 and 1997. Logistic regression analyses were conducted on the impact of the ADLS intervention on self-reported drinking-driving and knowledge of the ADLS. After introduction of the ADLS, knowledge of the sanction increased significantly and self-reported driving after drinking decreased significantly. Time series analyses of fatally injured drivers with a positive blood alcohol level demonstrated a significant intervention effect of the new law. These data suggest that there was widespread public awareness of the new law, a corresponding drop in drinking-driving behaviour, and a resultant decline in alcohol-related collisions. Preliminary analyses also indicate that the deterrent impact of the law was greatest among lighter or more moderate drinkers. For the covering abstract see ITRD E106992.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".