Contrasting Stakeholders’ Perspectives on the First Full-Year School Street Initiatives in Ontario, Canada
Bibliographic record
Abstract
• A growing number of cities are experimenting with street closure interventions. • Support for or opposition to these interventions varies across stakeholders. • Parents and children commonly saw the need for and benefits of School Streets. • Motorists, whether as residents or parents, are less supportive of School Streets. • Implementation of these interventions requires attention to divergent perspectives. A growing number of municipalities in North America and globally are experimenting with various forms of street closure interventions to support non-motorists in reclaiming city streets as public spaces. While many interventions are episodic in nature, intensive interventions that operate daily for months or years are difficult to implement because they are more disruptive of the status quo and more likely to face opposition from influential stakeholders. The objective of this study was to capture and compare the perspectives of three distinct stakeholders – residents, parents, and children – regarding school street interventions that operated daily from September to June in two neighbourhoods in a mid-sized Canadian city. Resident and parent perspectives were captured using anonymous online surveys, while child perspectives were captured using focus groups. Children and parents from both neighbourhoods perceived a need for the intervention to eliminate the hazards posed by vehicular congestion around the school entrance. Both groups reported that the intervention increased safety for children as they come and go from school each day. Residents were less convinced that the intervention was necessary and reported increased congestion on neighbouring streets. There were notable differences in residents’ perspectives between the two neighbourhoods regarding perceived changes in safety and in their experiences of the interventions, which are likely attributable to differences in built form and pre-existing traffic patterns in each neighbourhood. Motorists, whether as parents or residents, were much less likely to observe the intervention as beneficial and pleasant, and more likely to report observing problems with how it operated. These findings offer critical insights for policy and practice for street closure interventions, including having an effective strategy for traffic management to minimize opposition, the value of pilot testing to build support, and centering children's needs and voices in efforts to reclaim streets as public space.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".