Trends in drinking-driving fatalities in canada - progress continues
Bibliographic record
Abstract
The scope and intensity of activity directed at the problem of drinking and driving was unprecedented in Canada during the 1980s. Public and political concern engendered a wide range of initiatives and, consistent with this activity, corresponding declines in the magnitude of the problem itself occurred. Between 1981 and 1989, the percent of fatally injured drivers with blood alcohol concentrations (BACs) in excess of the legal limit dropped by 31%. The decline observed in the 1980s was interrupted rather abruptly and significantly beginning in the 1990s when the percent of fatally injured drivers who were drinking increased. Since 1993, however, there has been a further decline in the incidence of fatally injured impaired drivers that has continued through 1997. The level achieved in 1997 (31% of fatally injured drivers with BACs over the legal limit) was the lowest point reached in the past three decades. Recent changes in the magnitude of the alcohol-fatal crash problem, however, have not been uniform across different groups of fatally injured drivers. This paper examines these trends in the alcohol-fatal crash problem in Canada. For the covering abstract see ITRD E106992.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".