L’alcool au volant, c’est criminel depuis 1921 !
Bibliographic record
Abstract
Bill C-51 passed by the Canadian Parliament in 1985 and the publicity surrounding this legislation led many people to believe that a new crime regarding impaired driving had been created. This Bill, however, was to simply increase the penalty for drunk driving in the case of a first conviction. In fact, the penal solution to the “problem” of drunk driving is not new; in 1921 the offence of impaired driving was first introduced into the Code. This article examines the evolution of the prohibition of driving under the influence of alcohol in the Canadian Criminal Code and the enforcement of this law in Canada, in Quebec and in Ontario from 1921 to 1973. The first part presents the evolution of legislation concerning impaired driving. It goes through six important phases and covers the period from 1921 to 1973. The second part presents the statistical data used in our study. We also consider the reliability and validity of the data used. In the last part, we analyze the implementation of the law on infractions relating to drunk driving in a state of drunkeness indictable offence and summary conviction offence driving under the influence of alcohol or a drug (indictable offence and summary conviction offence), and finally, refusal to furnish a sample of breath (summary conviction offence). In conclusion, we present several recommendations based on the results of our analysis.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.005 |
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".