Children's perspectives on neighbourhood barriers and enablers to active school travel: A participatory mapping study
Bibliographic record
Abstract
Abstract Children today are spending more sedentary time indoors than time playing and being active outdoors. The daily journey to and from school represents a valuable opportunity for children to be physically active through active school travel. The majority of research on children's active school travel omits children from the research process even though children interpret their environments in fundamentally different ways than adults. Our research uses innovative participatory mapping and qualitative GIS methods to examine how children's perceptions of their environments influence their school journey experiences. Through our thematic analysis of 25 map‐based focus groups, we identified three main themes characterizing barriers and enablers to active school travel: safety‐related, material, and affective features. By positioning children as experts of their environments in our participatory methodology, our findings provide an important counterpoint to the adultist privilege characterizing the majority of research on children's active school travel. Environmental features that mattered for children's school journeys took on multiple meanings in their eyes, demonstrating that children's perspectives must be engaged to inform interventions to promote active school travel. We thus argue that identifying barriers and enablers to active school travel for children requires engaging children's views.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".