When science meets policy: co-construction of decision making [recidivist drink drivers]
Bibliographic record
Abstract
The legislation in the province of Quebec stipulates that a driverrs license is a privilege that drivers can be deprived if they are determined to represent a road safety risk. Until June 2012, only convicted DWI offenders were assessed to evaluate such risk. This delayed procedure was deemed insufficient. Accordingly, the Societe drassurance automobile du Quebec, Quebecrs licensing authority, introduced a change in their procedures. They introduced an assessment procedure to identify elevated risk for DWI recidivism upon arrest for drivers who had: 1) a blood alcohol concentration equal or superior to 160mg per cent; 2) a previous conviction for DWI within the last 10 years; 3) a refusal to provide a breath sample. This strategy did not eliminate a more fundamental shortcoming. The validity of prediction of DWI risk in first-time arrested drivers is inconclusive in the current state of knowledge. To palliate these limitations, the SAAQ assembled a Working Group composed of: 1) the coordinator of the current assessment program and her assistant; 2) three scientists experienced in prediction of DWI recidivism; 3) two clinicians experienced in the assessment of convicted DWI offenders; 4) a highly qualified research professional; 5) professionals of the research unit of the SAAQ.
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 | 0.235 | 0.363 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.019 | 0.032 |
| Scholarly communication | 0.033 | 0.019 |
| Open science | 0.009 | 0.028 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".