Exploring the Planning and Implementation of a School Street Initiative in Canada: A Pilot Study in Kingston, Ontario
Bibliographic record
Abstract
In Canada, only 20% of children use active modes of travel to get to and from school (Cottagiri et al., 2021). To address children’s growing automobile dependency, many cities are introducing initiatives and programming that aim to increase opportunities for children’s active transportation (Mackett et al., 2005; Pinkerton et al., 2013; Turner et al., 2013). Many of these initiatives aim to target children’s travel to school with the hope that this will provide an opportunity for physical activity as well as build children’s capacity for independent mobility and in turn lead to greater use of active travel in other trips. School Streets are one type of initiative being explored to promote active transportation, by closing streets around elementary schools to through traffic for 30-60 minutes before and after the school day. The evidence generated from School Street pilot programs suggests that they offer numerous benefits to communities, including safer neighbourhoods, increased active transportation, improved air quality in the schoolyard and an increase in children’s independent mobility (8 80 Cities, 2019; Davis, 2020; Wu & Bergeron, 2015). However, there have been very few School Street pilots in Canada and therefore there is limited data on the preparation involved in implementing these initiatives. A School Street pilot in Kingston Ontario was evaluated using field notes to understand the planning and implementation processes involved in launching a long-term School Street. Eight main processes were identified in the planning and implementation of the School Street including; building a relationship with the municipality, establishing a school site, mobilizing support from the school community, informing and consulting residents, gaining city approval, mobilizing volunteers and equipment, and modifying based on feedback. The longest processes in the planning stage, in terms of time, were establishing a willing school site and gaining city approval. The emergent findings and recommendations from this report offer guidance for other jurisdictions that are interested in launching a School Street initiative. School Streets are a low-cost active school travel initiative that are relatively easy to implement and should continue to be piloted across Canada to increase engagement in active school travel while maximizing the safety of students coming and going from school.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".