THE CONTRIBUTION OF ALCOHOL AND OTHER DRUGS AMONG FATALLY INJURED DRIVERS IN QUEBEC: SOME PRELIMINARY RESULTS
Bibliographic record
Abstract
This paper presents some preliminary results regarding the contribution of alcohol and other drugs in fatal crashes in Quebec. The data comes out of two sources. Over the past century, alcohol has been identified as the most problematic drug on the road while other drugs have received little attention. As elsewhere, the contribution of alcohol to fatal crashes has substantially decreased in Quebec over the last two decades. That improvement on the alcohol front has raised the issue of a substitution risk, from alcohol to other drugs. Facing this situation, the Societe de l'assurance automobile du Quebec (SAAQ), a Quebec government agency responsible for road safety promotion decided to undertake a major endeavor in order to establish the role of alcohol and other drugs in traffic crashes in Quebec. The research plan integrates the results of tow different analyses. The first one uses a case-control approach, which compares drug presence in fatally injured drivers to drugs detected in drivers participating in a roadside survey. The second one is a responsibility analysis (case-case approach) that compares drug cases to drug-free cases. The paper focuses on the role of alcohol and other drugs among fatally injured drivers using the data available at the end of 2001.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".