Is It Safe? How Does Safety Play a Role in a Child’s Mode of Travel Between Home and School?
Bibliographic record
Abstract
This study examined how both personal and traffic safety influenced a child’s mode of travel to/from school and how these concerns varied by population, built and social environment. Semi-structured interviews were conducted (n=37) with parents and their children at four schools of contrasting built and social environments within the City of Toronto. Thematic analysis of the interview transcripts was conducted. Comparative analyses of the data were explored by the environment (built and social), population (parent and child) and mode of travel. Personal safety concerns such as ‘stranger danger,’ bullies and dogs, along with traffic safety at street crossings and around the school emerged as the primary concerns for parents and children. It is difficult to alleviate parental fears of strangers, although they do decrease as the child gets older. For children, traveling in groups and ensuring dogs are on leashes can reduce personal safety fears. Personal safety was more of a concern in low income neighborhoods, whereas traffic safety was much more prevalent of an issue in inner-suburban areas. Furthermore, traffic concerns were more of an issue for non-active travellers. The findings of this research give evidence that mode choice is associated with safety and these concerns vary by environment, population and mode of travel.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".