Urban School Travel: Exploring Children's Qualitative Narratives about Their Trip to School
Bibliographic record
Abstract
A wide range of correlates of active school transport (AST) have been studied including demographic, individual and family factors, school factors, and social and physical environmental factors. Children's qualitative experiences of AST or non-AST have received less attention in the AST literature. This paper seeks to redress this imbalance. We present findings from children's qualitative narratives about their journeys to and from school in four specific built environments in Toronto, Canada. Forty-one children—21 who walked to school (AST) and 20 who were driven to school (non-AST)—in grades four, five, and six from four elementary schools consented to participate. We selected the schools based on the design of the built environment and socio-economic status. We began our study with some assumptions about how the built environment might impact on AST; however, once we immersed ourselves in the children's qualitative stories, we realized that their relationships to the built environments' were not straightforward. Children responded to our questions about health and wellbeing, neighborhoods, social relationships, personal safety, traffic and environmental conservation in interesting and nuanced ways. We conclude that future research on AST should continue to consider the heterogeneity of children's social and geographic relationships to, and experiences of, the built environments that may impact on their everyday school 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".