The effects of drinking‐driving laws: a test of the differential deterrence hypothesis
Bibliographic record
Abstract
AIMS: Ontario introduced an Administrative Driver's Licence Suspension (ADLS) law in 1996, whereby a person with a blood alcohol level over the legal limit of 80 mg%, or who refused to provide a breath sample, would have his or her driver's licence suspended immediately for a period of 90 days. We test the differential deterrence hypothesis which would predict that social or lighter drinkers would be more affected by the Administrative Driver's License Suspension law than heavier drinkers. DESIGN: Data from the 1996 and 1997 cycles of the Ontario Drug Monitor, a general population survey of Ontario adults (monthly cross-sectional surveys), were employed (response rate 64-67%). Analyses were restricted to drivers who reported at least some drinking during the last year (n = 3827). The total number of drinks consumed during the past 12 months was analysed with analysis of variance. FINDINGS: We found that the mean alcohol consumption of those who reported drinking-driving increased significantly after the ADLS was introduced, whereas the alcohol consumption of those who did not drive after drinking remained the same. CONCLUSIONS: The limits of this study include a lack of comparison data from regions without ADLS, a reliance on self-report measures, possible age restrictions of the findings and the fact that only an inferential test of the differential deterrence hypothesis is permitted by the data. Despite these limitations, these findings are consistent with the prediction that lighter or more moderate drinkers will tend to stop driving after drinking completely, and thus drop out of the drinking-driving population when the ADLS law was introduced, leaving heavier drinkers in this population. It will be important to continue to examine the dynamics of differential deterrence over a longer interval.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".