Why this work is in the frame
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Bibliographic record
Abstract
Considerations Policies to reduce the prevalence of drug-impaired driving should prioritize public health by establishing regulations on public access to cannabis and the consumption of cannabis in public spaces. To address misperceptions associated with cannabis use and driving, public education campaigns should incorporate clear, unambiguous messaging about the impairing effects of cannabis on driving.An emphasis should also be placed on the legal consequences of drugimpaired driving. Sanctions for drug-impaired driving are the same as those established for alcohol-impaired driving.These can include administrative sanctions (e.g., immediate roadside licence prohibitions), criminal sanctions or a combination of both. To increase law enforcement's capacity to detect drug-impaired drivers, policy makers should invest in enhanced training for police officers to recognize the common signs and symptoms of drug impairment, in addition to training on the use of approved oral screening devices. To reduce repeat violations of drug-impaired driving laws, prevention efforts should focus on addressing underlying problematic drug use, through treatment programs designed to meet the specific needs of drug-impaired drivers. The IssueTo coincide with the passing of Bill C-45, effectively legalizing cannabis for non-medical use in Canada, Bill C-46 made amendments to Canada's drug-impaired driving legislation in an effort to deal with the use of cannabis and other drugs by drivers.Bill C-46 outlines several new measures to assist law enforcement personnel in identifying drivers impaired by cannabis.In addition, the bill includes measures that will affect drinking drivers.While policy makers can draw from existing alcohol and tobacco legislation to guide the development of evidence-informed policies for cannabis, legislation must reflect the unique characteristics of cannabis and the risks and harms associated with cannabis-impaired driving.This brief outlines the key issues for those involved in establishing effective policies to minimize the harms associated with driving under the influence of cannabis.It provides policy makers at the municipal and provincial levels with the information and tools necessary to develop evidenceinformed policy about cannabis and driving.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.026 |
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 it