The Role of Driver Education in the Licensing Process in Quebec
Bibliographic record
Abstract
PROBLEM: In many jurisdictions, driver education (DE) graduates, compared to non-graduates, are granted a time-discount that allows them to drive unsupervised several months earlier, despite little evidence of a safety benefit and consistent evidence of increased crash risk. Confounding factors may be threatening the validity of DE evaluations. A theoretical framework called the "licensing process" (LP) is proposed to identify and explore potential confounding factors in DE evaluations. METHOD: Prospective study data on a cohort of 1804 novice drivers 16 to 19 years of age of both sexes are analyzed in relation to the LP framework. These data derive from two sources that were linked together: an extensive questionnaire on learning methods, risk-taking, and lifestyles, and government records on exam performance, violations, and crashes. RESULTS: Violation and crash records are not associated with DE attendance. DE attendance is associated with younger ages, greater financial support from family, and fewer hours of supervised driving practice with a learner's permit. For both sexes, more hours of supervised driving practice with a learner's permit is associated with increased crash risk. Most participants, particularly males under 19 years of age, attended DE partly or entirely to save time or money; these motivations are associated with higher violation and crash rates. DISCUSSION: DE evaluations need to identify and control for potential confounding factors. Research is needed to understand the associations between increased crash risk and potential confounding factors like motivation to attend DE and hours of supervised driving practice.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".