Intervention analysis of the safety effects of a legislation targeting excessive speeding in Canada
Bibliographic record
Abstract
Excessive speeding is a major traffic safety concern consequently, numerous countermeasures have been considered to mitigate this problem. Excessive speeding, street racing and stunt driving activities subject all road users to extreme risk. To address this problem, three Canadian provinces introduced severe sanctions against drivers who exceed speed limits by high margins. Under the laws offenders were subject to immediate license suspension and vehicle impoundment. In this paper, intervention analysis of the collision data from the three provinces was conducted to identify the safety effects of the legislation. The analysis aims to identify changes in the time series behaviour of collision data after the adoption of the law. The changes were assessed for statistical significance, and the magnitude of the change was quantified. In general, the paper showed that the legislative changes were associated with drops in province-wide fatal collisions substantiating the safety benefits of introducing such legislation against excessive speeders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".