Reconstruction Ahead: School Streets and Street Reclamation in Ontario
Bibliographic record
Abstract
Streets are public spaces, yet they remain the domain of motorists. Contrary to this, people continue to challenge the narrative. One strategy has been through\nstreet reclamation efforts, referring to how streets can be reclaimed psychologically and physically from motorists to improve health, social connections, and community well-being. One type of street reclamation effort is called a School Street, a street experiment restricting vehicles on the street in front of a school at the start and end of the school day to create a car-free environment. School Streets have been organized globally in the past decade, with at least five implemented in Ontario since 2019. \n\nThe Reconstruction Ahead report investigates the implementation of Schools Streets in Ontario to reveal the implications for broader street reclamation efforts. To reach this goal, those who led every known School Street in Ontario and other relevant interest groups were interviewed to investigate the barriers faced and potential scaling solutions. The experience of the author of this report, as a School Street implementer in Kingston, Ontario, was also captured, ensuring the information was grounded by first-hand experience.\n\nBased on the findings, this report has documented barriers that hinder School Streets’ establishment, scale, and sustainability; proposed recommendations for\nmunicipalities to establish, scale, and sustain School Streets in Ontario; and names long-term implications for contemporary street reclamation efforts.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".