Are school zones effective? An examination of motor vehicle versus child pedestrian crashes near schools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To analyse the relationships between factors related to school location and motor vehicle versus child pedestrian collisions. METHODS: Data on all police-reported motor vehicle collisions involving pedestrians less than 18 years of age that occurred in Toronto, Canada, between 2000 and 2005 were analysed. Geographic information systems (GIS) software was used to assess the distance of each collision relative to school location. The relationships between distance from school and collision-related factors such as temporal patterns of school travel times and crossing locations were analysed. RESULTS: Study data showed a total of 2717 motor vehicle versus child (<18) pedestrian collisions. The area density of collisions (collisions/area), particularly fatal collisions, was highest in school zones and decreased as distance from schools increased. The highest proportion of collisions (37.3%) occurred among 10-14-year-olds. Within school zones, collisions were more likely to occur among 5-9-year-old children as they travelled to and from school during months when school was in session. Most collisions within school zones occurred at midblock locations versus intersections. CONCLUSIONS: Focusing interventions around schools with attention to age, travel times, and crossing location will reduce the burden of injury in children. Future studies that take into account traffic and pedestrian volume surrounding schools would be useful for prevention efforts as well as for promotion of walking. These results will help identify priorities and emphasise the importance of considering spatial and temporal patterns in child pedestrian research.
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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 | 0.001 |
| 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 it