THE DRAG-2 MODEL FOR QUEBEC. IN: STRUCTURAL ROAD ACCIDENT MODELS. THE INTERNATIONAL DRAG FAMILY
Bibliographic record
Abstract
Development of a highway infrastructure plays a major role in any economy, including Quebec's by improving transportation of people and goods. Development does, however, come at a cost, namely air and noise pollution and road accidents. Road accidents directly affect the Societe de l'assurance automobile du Quebec (SAAQ) at tow levels. The SAAQ is the agency set up by the Government of Quebec in March 1978 to oversee its new no-fault public insurance scheme for bodily injuries sustained by Quebecers in road accidents. A few years later, given its financial responsibility to Quebecers, in 1980 the government transferred to the SAAQ the mandate of controlling access to the road network through regulation of drivers and vehicles and promotion of road safety in order to fulfill these mandates, the SAAQ must focus on minimizing bodily injuries from road accidents by promoting appropriate safety measures. It is therefore essential that the SAAQ have the necessary tools to better understand all aspects of road safety. To better understand trends in road accidents, in 1983-1984 the SAAQ granted funding to the University of Montreal, under its road safety research program, to develop an effective means of analysis. This initiative led to version 1 of the DRAG econometric model. Given the valuable results obtained and the potential for this analysis tool, the DRAG-2 model was developed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".