Evaluation of the Victorian Safe Driving Program (SDP) for Hoon Drivers
Bibliographic record
Abstract
This study aimed to determine whether the Safe Driving Program (SDP), a behaviour change program ordered by the courts operating in Victoria, Australia, was successful in reducing the offending and crashes by hoon drivers. A sample of 3,324 hoon drivers who completed the SDP were compared with 1,063 hoon drivers who were ordered to complete the program but failed to do so. These two groups were also compared with a third group consisting of 30,678 hoon drivers who, for various reasons, had not been ordered to complete the SDP. Longitudinal group comparisons were made regarding overall, hoon and serious offending, as well as the number of crashes, fatalities and serious injuries and the proportion of offenders receiving bans and vehicle impoundments. Generalised Estimating Equations were used for this purpose, providing estimates of group differences. It was found that statistically significant benefits were obtained through the placement of SDP orders. However, differences between the offenders that completed their SDP order and those that failed to complete their SDP order were not always as expected. Reasons for this are explained and implications for the program, policies and penalties are discussed. No changes are recommended for current impoundment and SDP arrangements.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".