Drivers in alcohol related crashes in Saskatchewan
Bibliographic record
Abstract
Media reports of fatal crashes involving alcohol often leave the public with the impression that most impaired drivers lwalk awayr following a crash that has killed one or more innocent victims. While such stories are tragic, they are not necessarily representative of all alcohol-related fatal crashes. Impaired drivers are most likely to be the victim. These two very different crash scenarios may differ in terms of the characteristics of the impaired drivers involved and the circumstances of the crash itself. The purpose of this study was to test the hypothesis that impaired drivers who survive fatal crashes do not differ from impaired drivers who die in fatal crashes in terms of circumstances of the crash or the characteristics of the drivers. Drivers involved in fatal crashes in the province of Saskatchewan from 1996 through 2009 were divided into groups according to alcohol status (no alcohol, had been drinking, BAC over 80 mg/dL) and crash survival. In the case of high BAC drivers, the driver was the most likely to die followed by a vehicle occupant. Driver records were also assessed by high and low BACs. In the case of surviving drivers their prospective record was also assessed. The findings serve to highlight the risks impaired drivers pose to themselves as well as other road users. The observed differences in impaired drivers who die in crashes and those who survive a fatal crash are of value in the development of public awareness and prevention programs as well as enforcement efforts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".