Past and Present of Active School Transportation: An Exploration of the Built Environment Effects in Toronto, Canada from 1986 to 2006
Bibliographic record
Abstract
The health benefits of walking and cycling to and from school, also called active school transportation (AST), are well documented. In the context of a declining trend in AST across the Western world, this paper examines school-travel behavior of 11-year-old children in Toronto, using multiple cross-sectional data from 1986, 1996, and 2006 Transportation Tomorrow Surveys. Results from binomial logit models suggest that school-travel distance and neighborhood built environment indeed explain some variation in the odds of AST between 1986 and 2006, and that the correlates of AST may have changed over time. Higher neighborhood block density correlated with walking/cycling in 1986. In contrast, household automobile ownership was negatively associated with AST in 2006; the effect of the built environment was relatively weak for that year. In addition, fewer children walked/cycled in 2006 compared to 1986, even when distance to school was short (<0.8 kilometers). Policy and programs should recognize the potentially changing role of travel distance to school and automobile ownership on a child’s school travel outcome. Interventions in neighborhoods with high automobile ownership should specifically focus on education and encouragement to increase AST rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".