Altering School Attendance Times to Prevent Child Pedestrian Injuries
Bibliographic record
Abstract
OBJECTIVES: The purpose of this research was to determine whether modifying school start time schedules can be used to reduce children's exposure to traffic on their morning walks to school. METHODS: We use models of pedestrian and motor vehicle commuting to estimate the frequency of encounters between child pedestrians and motor vehicles at intersections throughout the City of Hamilton, Ontario, Canada. We use a simple heuristic to identify the school-specific start times that would most reduce the local frequency of encounters between motor vehicles and pedestrians. RESULTS: Our analysis suggests that it may be possible to achieve an almost 15 percent reduction in the total number of encounters between child pedestrians and motor vehicles during the morning commute by staggering school start times such that the periods of high pedestrian activity are temporally staggered from periods of high motor vehicle activity. Our analysis suggests that small changes in school start times could be sufficient to see noteworthy reductions in pedestrian exposure to traffic. CONCLUSIONS: Changing school times may be an effective, inexpensive, and practical tool for reducing child pedestrian injuries in urban environments. Enhanced transportation models and community-based interventions are natural next steps for exploring the use of school-specific scheduling to reduce the risk of child pedestrian injury. Further research is required to validate our models before this analysis should be used by policy makers.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".