The Case for Comprehensive Random Breath Testing Programs in Canada: Reviewing the Evidence and Challenges
Bibliographic record
Abstract
Impairment related crashes remain Canada’s leading criminal cause of death. In response, this article examines impaired driving rates and enforcement in Canada and argues that random breath testing programs would increase the risk of apprehension, thereby enhancing the deterrent impact of Canada’s impaired driving laws. The authors analyze the international experience with random breath testing, explaining that most developed and developing countries, including Australia, New Zealand, and Ireland have implemented random breath testing. These programs have had significant traffic safety benefits and enjoy broad public support. The authors argue that, while random breath testing legislation may be found to infringe section 8 and is most likely to infringe sections 9 and 10(b) of the Canadian Charter of Rights and Freedoms, it should be upheld under section 1. They argue that the potential benefits of random breath testing in Canada would be substantial, while the effects on individual rights would be modest.
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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 | 0.046 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".