Evaluation of Deterrent Impact of Ontario's Street Racing and Stunt Driving Law on Extreme Speeding Convictions
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to conduct a process and outcome evaluation of the deterrent impact of Ontario's street racing and stunt driving legislation, introduced in September 2007, on extreme speeding convictions. It was hypothesized that because males are much more likely to engage in speeding, street racing, and stunt driving, the new law would have more impact in reducing extreme speeding in males compared to females. METHODS: Descriptive statistics and time series plots were used for the suspensions data. Interrupted time series analysis with autoregressive integrated moving average (ARIMA) modeling was applied to the monthly extreme speeding convictions in Ontario for the period of January 1, 2003, to December 31, 2011, to assess the impact of the new legislation, separately for male drivers (intervention group) and female drivers (comparison group). RESULTS: The results indicated that per licensed driver, 1.21 percent of 16- to 24-year-old male drivers and 0.37 percent of 25- to 64-year-old male drivers had their licenses suspended between September 2007 and December 2011. This is in contrast to female drivers: 0.21 percent of 16- to 24-year-old female drivers and 0.07 percent of 25- to 64-year-old female drivers had their licenses suspended during the same time period. A significant intervention effect of reduced extreme speeding convictions was found in the male driver group, though no corresponding effect was observed in the female driver group. The findings of this study are consistent with previous research on demographics of street racers and stunt drivers. CONCLUSIONS: These findings are congruent with deterrence theory that certain, swift, and severe sanctions can deter risky driving behavior and support the hypothesis that legal sanctions can have an impact on the extreme speeding convictions of the intervention group.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".