The effect on collisions with injuries of a reduction in traffic citations issued by police officers
Bibliographic record
Abstract
OBJECTIVE: To assess the effect on collisions with injuries of a 61% reduction in the number of traffic citations issued by police officers over a 21-month period. METHODS: Using descriptive analyses as well as ARIMA intervention time-series analyses, this study estimated the impact of this reduction in citations issued for traffic violations on the monthly number of collisions with injuries. RESULTS: Simple descriptive analysis reveals that the 61% reduction in the number of citations issued for traffic violations during the experimental period coincided with an increase in collisions with injuries. Results from the interrupted time-series analyses reveal that, on average, eight additional collisions with injuries occurred every month during which the number of tickets issued for traffic violations was lower than normal. As this pressure tactic was applied for 21 months, it is estimated that this situation was associated with approximately 184 additional collisions with injuries: equivalent to 239 traffic injuries (either deaths, minor or serious injuries). CONCLUSION: In the province of Quebec, police officers are an important component of road safety policy. Issuing citations prevents drivers from adopting reckless driving habits such as speeding, running red lights and failing to fasten their seat belt.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".