Understanding drinking and driving reforms: a profile of Ontario statistics
Bibliographic record
Abstract
Drinking and driving has been the subject of considerable public concern and legislative attention in many countries in recent years. In Canada, the federal criminal laws and provincial traffic acts have been significantly amended since the mid-1980s: police investigatory powers have been broadened, new federal crimes and provincial offences have been enacted, and more onerous penalties and administrative sanctions have been introduced. The most recent cycle of Canadian legislative reform has focused on increasing sanctions, particularly for repeat offenders. Federal Criminal Code amendments in 19991 increased the minimum fines and driving prohibitions for the three most common offences—impaired driving, driving with a blood alcohol level (BAL) above 0.08%, and failing to provide breath or blood samples. Significant changes have also occurred at the provincial level. For example, Ontario introduced legislation which, when fully implemented, will impose indefinite licence suspensions on those convicted of three federal drinking and driving offences within 10 years,2 and British Columbia has followed suit.3 Both the federal and provincial governments have widely publicized their “get tough” legislation.4,5 However, Mothers Against Drunk Driving (MADD) Canada and other organizations have questioned whether these initiatives will have a significant impact. Stiffer penalties are unlikely to have much effect if the police do not have sufficient resources to apprehend and charge drinking drivers,6,7 or if prosecutors are so overburdened that they can only act in the most serious and blatant cases.8 We had hoped that a review of Ontario's drinking and driving statistics would shed some light on enforcement, prosecutorial and sentencing practices, and on the likely impact of the new “get tough” legislation. However, it was surprisingly difficult to obtain much information from the provincial government. Although Ontario published statistics on its federal drinking and driving convictions, it did not publish …
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.048 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".