Bibliographic record
Abstract
It is possible to eliminate death and serious injury from Canada’s roads. In other jurisdictions, the European Union, centres in the United States, and at least one automotive company aim to achieve comparable results as early as 2020. In Canada, though, citizens must turn their thinking on its head and make road safety a national priority. Since the motor vehicle first went into mass production, the driver has taken most of the blame for its failures. In a world where each person’s safety is dependent on a system in which millions of drivers must drive perfectly over billions of hours behind the wheel, failure on a massive scale has been the result. When we neglect the central role of the motor vehicle as a dangerous consumer product, the result is one of the largest human-made means for physically assaulting human beings. It is time for Canadians to embrace internationally recognized ways of thinking and enter an era in which the motor vehicle by-product of human carnage is relegated to history. No Accident examines problems related to road safety and makes recommendations for the way forward. Topics include types of drivers; human-related driving errors related to fatigue, speed, alcohol, and distraction and roads; pedestrians, cyclists, and public transit; road engineering; motor vehicle regulation; auto safety design; and collision-avoidance technologies such as radar and camera-based sensors on vehicles that prevent crashes. This multi-disciplinary study demystifies the world of road safety and provides a road map for the next twenty years.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.541 | 0.364 |
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".