CANADA'S STRATEGY TO REDUCE IMPAIRED DRIVING - EXPERIENCE TO DATE AND FUTURE ASPIRATIONS
Bibliographic record
Abstract
In Canada, over the past 20 years, significant progress has been made in reducing the number and severity of road crashes in Canada despite an increase in the number of drivers, vehicles and estimates of kilometres driven on Canadian roads. While this progress is significant and laudable, the number of people killed or injured on roads in Canada is still unacceptably high. The leading contributor to deaths on our roads is impaired driving. Each year, alcohol-related crashes contribute to as much as 40 % of traffic deaths. Thus, a key area of concern in addressing traffic collision casualties is the management of the issue of impaired driving and its consequences. This paper gives an overview of past, current and future work that is being done at the provincial and national levels to help address the problem of drinking and driving. We conclude that over the 11 years since its inception, Canada’s Strategy To Reduce Impaired Driving (STRID) has facilitated the development of key pieces of anti-drinking and driving infrastructure in the different Canadian jurisdictions. Recent enhancements to this strategy are aimed at capitalizing on the components of this infrastructure to reduce the magnitude of drinking and driving and its adverse consequences in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".