Reclaiming urban space for children: Examining the impact of School Streets on independent mobility and active transport
Bibliographic record
Abstract
INTERVENTION: School Street initiatives are traffic-free zones around the entrances of primary schools designed to make it safer for children to come and go from school. Beyond increasing safety, these initiatives offer opportunities for children to increase their engagement in active school travel and to become more independently mobile. RESEARCH QUESTION: How does the implementation of a School Street intervention influence children's active travel and independent mobility on their way to school? METHODS: A School Street intervention was implemented every Friday at a primary school in Montreal from September 2022 to June 2023. Bi-monthly direct observations were conducted to collect data on children's modes of travel and whether they travelled independently or with an adult before the start of classes. RESULTS: Independent, active travel was more frequently observed on School Street days compared to non-School Street days. Additionally, a progressive increase in active transportation and independent mobility was observed throughout the school year, regardless of School Street designation. CONCLUSION: This study underscores the potential impact of School Street interventions on enhancing children's active school travel and independent mobility. Establishing safe zones near schools can positively influence children's commuting behaviours, fostering healthier and more autonomous travel habits.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".