Drinking‐driving fatalities and consumption of beer, wine and spirits
Bibliographic record
Abstract
Drinking-driving is a leading cause of preventable morbidity and mortality in Canada. The purpose of this paper was to examine factors that influenced drinking driver deaths in Ontario. We examined the impact of per capita consumption of total alcohol, and of beer, wine and spirits separately, on drinking-driving deaths in Ontario from 1962 to 1996, as well as the impact of the introduction of Canada's per se law and the founding of People to Reduce Impaired Driving Everywhere - Mothers Against Drunk Driving (PRIDE - MADD) Canada. We utilised time-series analyses with autoregressive integrated moving average (ARIMA) modelling. As total alcohol consumption increased, drinking driving fatalities increased. The introduction of Canada's per se law, and of PRIDE-MADD Canada, acted to reduce drinking driving death rates. Among the specific beverage types, only consumption of beer had a significant impact on drinking driver deaths. Several factors were identified that acted to increase and decrease drinking driver death rates. Of particular interest was the observation of the impact of beer consumption on these death rates. In North America, beer is taxed at a lower rate than other alcoholic beverages. The role of taxation policies as determinants of drinking-driving deaths is discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".