Reforming Canada's new drug-impaired driving law: the need for per se limits and random roadside screening
Bibliographic record
Abstract
Unfortunately, the 2008 Criminal Code amendments, which authorized Canadian police to demand Standardized Field Sobriety Tests and Drug Recognition Evaluations, have not had their desired effects. The measures have proved to be costly, time-consuming and cumbersome, and are readily susceptible to challenge in the courts. Accordingly, the charge rates for drug-impaired driving remain extremely low. To review alternative enforcement models for drug-impaired driving that have been adopted in other jurisdictions, and to recommend a model that will improve apprehension and conviction rates and thereby deter drug-impaired driving. The model must be consistent with Canadars social, political and constitutional frameworks. Canada should adopt a system of random roadside saliva screening, similar to the model used in Victoria, Australia. For drivers who test positive, more sensitive evidentiary testing should be conducted at a police station, after the driver has been afforded the right to counsel. The 2008 Criminal Code amendments were an important first step, but will not significantly improve apprehension or conviction rates for drug-impaired driving. It is preferable to set per se limits for the most commonly-used drugs, enforceable through a system of screening and evidentiary tests. This will be more efficient and cost-effective, and will result in more reliable evidence for criminal trials. Although this system will inevitably be subject to constitutional challenge, existing case law suggests that it should be upheld as a reasonable limit on constitutional rights.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".