Bibliographic record
Abstract
No Accident: Eliminating Injury and Death on Canadian Roads addresses the carnage (28,000 Canadian lives lost in the past ten years and many more serious injuries) by a systemic analysis of the wide range of "causes of safety" that could be implemented or intensified.Indeed, Canada can advance lifesaving conditions on its highways and in its motor vehicles simply by catching up to the nineteen countries whose record by many measures is better.Author Neil Arason rejects the fatalistic attitude behind the word "accident" and in very interesting manners brings readers to understand that not all traffic safety measures are equal.Easier than expecting all starting points to be driver behaviours, with all their variables and temperaments, are the systemic engineering approaches of vehicle crashworthiness, vehicle collision avoidance, safer roadways, and such stable regulatory adjustments to the human element-which he does not neglect.From the 1960s on, when long-suppressed crashworthiness became mandatory safety standards-seat belts, collapsible steering columns, padded interiors, stronger rollover and side protections, head restraints, and later air bags-the fatality tolls declined in response.But after additional improvements in brakes and tires, the auto company lobbyists slowed the regulatory incorporation of ready and feasible safety technologies to a near standstill in the 1980s, 1990s, and 2000s.This produced a backlog that Mr. Arason believes should become the basis for a renewed national motor vehicle highway safety mission that reduces the horrendous costs-human, economic, and psychological on victims, their families, and society at large.This is not a narrow-gauged book.Instead, it is a very well-written and documented story, comprehensive in scope, motivating in design, and elevating in its global humanitarian purposes.Mr. Arason also pays attention
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.703 | 0.658 |
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".