The criminalization of impaired driving in Canada: assessing the deterrent impact of Canada's first per se law.
Bibliographic record
Abstract
OBJECTIVE: The goal of this article is to assess the effectiveness of Canada's first per se law criminalizing driving with a blood alcohol concentration of over 0.08%, the Breathalyser Law introduced in 1969, in reducing drinking-driver-related fatalities. We also examine the long-term deterrent effect of this law on driver fatality rates. In the analyses we include such potentially confounding influences on drinking-driver fatality rates as the founding of Mothers Against Drunk Driving (MADD), Canada; the introduction of Ontario's mandatory seatbelt law; per capita alcohol consumption; the unemployment rate; vehicles registered per capita; and precipitation rates. METHOD: Interrupted time series analysis with auto-regressive integrated moving average modeling was applied to the annual number of motor vehicle driver fatalities in Ontario for the period 1962-1996 to examine drinking- and nondrinking-driver fatalities. RESULTS: A significant intervention effect was found for the Breathalyser Law in Ontario, which was associated with an estimated reduction of 18% in the number of fatally injured drinking drivers. No corresponding effect was observed for nondrinking-driver fatalities. Per capita alcohol consumption was positively associated with drinking-driver fatalities; Ontario's mandatory seatbelt law was linked to nondrinking-driver fatalities; and the formation of MADD, Canada, was strongly associated with drinking- and nondrinking-driver fatalities. CONCLUSIONS: These data provide evidence that Canada's per se law had a specific deterrent effect that resulted in a reduction in drinking-driver fatalities. A long-term deterrent effect was also observed, which is in contrast to the early policy literature on impaired driving.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".