Evaluation of the Effectiveness of Driver Improvement Programs in Reducing Future Crashes
Bibliographic record
Abstract
This paper addresses the statistical analysis of driver record data to evaluate driver improvement programs in Ontario, Canada. Several before-after studies were conducted to assess the effectiveness of various interventions in reducing future crashes. The results of these analyses indicated that drivers who receive warning letters, demerit point related interviews or first or second demerit point license suspensions on average have fewer crashes in the period after the intervention than drivers with similar records who were not subject to the intervention, indicating that these interventions are effective in reducing crashes. Drivers receiving warning letters were estimated to have 7.5% fewer crashes after receiving warning letters compared to drivers with similar records. For demerit point related interviews, the first suspension and second suspensions, the estimated reductions are 11.9%, 27.8%, and 42.7% respectively. All of these results are statistically significant at the 95% confidence level. The results for a program aimed at high risk drivers aged 70 years and older (70+ program) and a collision repeater program also indicated that both programs are effective in that drivers subjected to these interventions have fewer subsequent crashes on average than drivers with similar records who were not subject to the intervention. For the 70+ program, the reduction is estimated to be 33% and for the collision repeater program 10.2%. However, for the collision repeater program the reduction, which was based on a very small sample size (113 drivers), was statistically insignificant at the 95% confidence level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".