Developments in Canadian community-based impaired driving initiatives: the Ontario "Last-Drink Program"
Bibliographic record
Abstract
A disproportionate number of impaired drivers come from licensed establishments, as opposed to their own homes or other private venues. lLast Drinkr programs focus liquor licensing enforcement on high-risk establishments in an attempt to reduce impaired driving and other alcohol-related incidents. Pursuant to the program, the police ask impaired driving suspects where they were drinking. If a licensed establishment is named, the information is forwarded to licensing officials for follow-up action. This paper outlines the rationale for implementing Last Drink programs and summarizes the limited research on their impact. It describes Ontariors mandatory Last Drink program and the preliminary results of the pilot project on which it was based. A large percentage of impaired drivers in the Ontario pilot project came from a small percentage of the licensed venues. The Alcohol and Gaming Commission of Ontario (AGCO) described the programrs preliminary results as lencouraging on multiple fronts,r and Mothers Against Drunk Driving (MADD) Canada and other safety organizations endorsed it. Ontario subsequently introduced a mandatory province-wide Last Drink program. Last Drink programs appear to temporarily improve serving practices in the targeted venues. However, it is unclear if these initiatives have a lasting impact on the targeted establishments, the broader hospitality industry or the incidence of impaired driving.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".