Random Breath Testing: A Canadian Perspective
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is to examine the case for and challenges to implementing random breath testing (RBT) in Canada, with a particular focus on the persistence of impaired driving under the current method of law enforcement. It seeks to place RBT within Canada's existing legal and social framework. METHODS: This article reviews Canada's impaired driving record, charge and conviction rates, and law enforcement challenges. It then summarizes the impact that RBT programs have had in comparable countries. Finally, it examines whether the enactment of RBT would be upheld under Canada's Charter of Rights and Freedoms. RESULTS: Canada has made little progress in reducing impaired driving since the late 1990s. Current enforcement methods fail to detect the majority of impaired drivers, even when stopped at sobriety checkpoints. This has reduced the perceived risk of apprehension and helps to explain the persistence of impaired driving in Canada. Faced with similar challenges, Australia, New Zealand, Ireland, and most EU countries have introduced comprehensive RBT programs. Comprehensive RBT has been shown to significantly reduce impaired driving deaths and injuries. Proposals to enact RBT in Canada will inevitably generate claims that it violates drivers' Charter rights. Similar arguments have been raised in opposition to RBT in other countries. This article demonstrates that RBT is compatible with the existing Charter case law involving traffic legislation and border, airline, and courtroom security. CONCLUSION: Experience in other countries indicates that RBT is a minimally intrusive, cost-effective, and publicly accepted impaired driving countermeasure and that it would significantly improve the detection and deterrence of impaired drivers. Moreover, RBT is compatible with the Charter.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".