Route-Based Analysis to Capture the Environmental Influences on a Child's Mode of Travel between Home and School
Bibliographic record
Abstract
This study examined environmental influences on a child's mode of travel between home and school. Grade 7 and 8 students (n = 614) from twenty-one schools throughout London, Ontario, participated in a school-based travel mode survey. Geographic information systems (GIS) were employed to examine environmental characteristics of the child's mode of travel between home and school measured at the scale of the likely travel route. Logistic regression was used to assess what factors influence both the to- and from-school trip. Over 62 percent of students living within 1.6 kilometers (1 mile) of school walked or biked to school and 72 percent walked or biked home from school. Actively commuting to school was positively associated with shorter trips, with distance being the most important correlate. Boys were significantly more likely to use active travel modes than girls. Higher traffic volume along the route was negatively related to rates of active travel and children from higher income neighborhoods were less likely to actively travel than children from lower income neighborhoods. In terms of environmental characteristics, the presence of street trees was positively associated and higher residential densities and mixed land uses were negatively associated with active travel to school. For the journey home, crossing major streets and increased intersection density were negatively associated with active travel. The findings of this research give evidence that active travel is associated with the environmental characteristics of walking routes. This information should be considered for urban planning and school planning purposes to improve children's walking environments.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".