Bibliographic record
Abstract
Each year, nearly one percent of Canadians is injured in traffic accidents. This paper focuses on the nature of road transportation risks in Canada ‐ the magnitude of the problem and the reasons for its persistence. A mobility‐based approach to road safety research is advocated because of its potential to both improve understanding and help work towards longterm solutions. The discussion is organized around two main themes: risks of mobility and mobility‐related risks. In the first instance, the focus is on linkages between quantity of travel/exposure and safety, especially over time. In the second theme, the emphasis is on linkages between quality of exposure and safety. Specific themes, including weather hazards and graduated licensing, are used to develop the second theme. The main conclusion is that casualty rates per unit of travel have declined over the past 25 years, largely due to improved engineering, enforcement and education, but that casualty levels and monetary costs remain high, largely because of the auto‐mobility imperative. Chaque année, près de 1% des canadiens se blessent dans des accidents de la route. Cette communication s'intéresse à la nature du risque routier au Canada, plus spécifiquement sur l'ampleur du problème et les raisons de sa persistance. Une approche axée sur la mobilité est préconisée pour son potentiel à apporter une meilleure compréhension du problème et à faciliter la mise en place de solutions à long‐terme. La discussion s'organise autour de deux thèmes centraux: 1) le risque de la mobilité et 2) les risques liés à la mobilité. En premier lieu, les liens entre la quantité des déplacements/exposition au risque et la sécurité sont investigués à travers le temps. Pour le deuxiéme thème, l'accent est mis sur les liens entre la qualité de l'exposition et la securité. Des thèmes spécifiques incluant les conditions météorologiques et l'expérience de conduite sont utilisés pour développer le deuxième thème. La principale conclusion est que les taux de blessures par unité de déplacement ont diminué au cours des 25 dernières années, ce qui est en grande partie attribuable au progrès en ingénierie, à la mise en place de mesures coercitives et à l'éducation. Toutefois, la gravité des blessures et les coûts monétaires qui y sont liés demeurent élevés principalement à cause des impératifs de la mobilité automobile.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".