SAFER WALKING ROUTES TO SCHOOL: APPLIED AND METHODOLOGICAL GEOGRAPHIES OF CHILD PEDESTRIAN INJURY
Bibliographic record
Abstract
The general theme of this dissertation is understanding and enabling safe walking routes to school for children. We restrict our focus to safety issues related to the motorized-transportation environment, thereby defining safety as a function of factors that determine whether or not a child will be struck by a motor-vehicle on their journey to or from school. Our analysis is unique because it is at a small geographical scale but is representative of an entire urban environment. Working at a small geographic scale allows us to evaluate the variability in safe routes for children within our study area and apply our findings to develop a decision support tool that could be used to plan individualized routes for children in other similar urban environments. Our study area for this dissertation is Hamilton, Ontario, Canada. The findings in this dissertation contribute ideas about how features of the local road environment may and may not influence risk of collisions between child pedestrians and motor-vehicles. It also offers methodological insight for future research on pedestrian safety at small geographic scales. This dissertation demonstrates the potential reduction in the risk of child pedestrian injuries by planning safer routes to school and also introduces methods that can be used to plan safer routes for children. Our results are a reminder of the importance of understanding the interaction between environment and behaviour in research on traffic safety and offer some caution to the notion of a universal 'safe route' to school. Whether or not a particular route to school is safe will very likely be dependent both on the environment and the child's behaviour in that environment.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".