Shortening the trip to school: Examining how children’s active school travel is influenced by shortcuts in London, Canada
Bibliographic record
Abstract
For children and youth, the journey to and from school represents a significant opportunity to increase daily levels of physical activity by using non-motorized modes of travel, such as walking and biking. Studies of active school travel have demonstrated that the likelihood a child will walk or bike is significantly influenced by the distance they must travel between home and school, which in turn, is influenced by built environment characteristics such as the configuration of the local road network. This study examines how shortcuts can facilitate active school travel by decreasing the distance children must travel to get to and from school. A geographic information system was used to compare shortest route distances along road networks with and without shortcuts in 32 elementary school zones in London, Ontario, Canada and provide evidence on the effectiveness of shortcuts to facilitate active school travel. This study contributes two key findings: (1) shortcuts have a greater impact in areas with low street connectivity and low population density and (2) children living farther from school are more likely to benefit from shortcuts. The findings suggest that planners should consider the location and maintenance of shortcuts in school neighbourhoods in order to promote increased physical activity, health and well-being among students.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".