GIS-based spatial analysis of child pedestrian accidents near primary schools in Montréal, Canada
Bibliographic record
Abstract
In Montréal, Canada, accidents affecting child pedestrians (5 to 14 years old) remained almost constant from 1994 to 1999 despite the great amount of prevention measures. Moreover, the elementary public school environment has been barely taken into account by past and present research on factors affecting the risk of accident even though children attend school most weekdays. We argue here, therefore, that the integration of the local environment into the spatial analysis of child pedestrian accidents could help to reduce them. Accordingly, we have integrated socio-economic and environmental data into a geographic information system in order to perform a geographically weighted regression and results demonstrate that the average network distance separating accident and closest school is less than 500 meters, thereby confirming a relationship of proximity between these two locations. Results also demonstrate the relevance of adding a spatial dimension to the regression model by suggesting that prevention initiatives should take into account the particular nature of each neighbourhood so that more relevant risk factors can be targeted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".