PW 0373 Evaluation of the vision zero school safety zones program in the city of toronto- policy makers and researchers working together
Bibliographic record
Abstract
The City of Toronto adopted its Vision Zero Road Safety Plan in July 2016, with its focus on eliminating motor vehicle collisions that result in death and serious injuries. The safety plan emphasizes a collaborative and integrative approach, involving multiple stakeholders. One of the Plan’s six areas of emphasis is on school children. Stakeholders from public health, the public school board, the police, a not-for- profit organization and academic researchers have worked with the City of Toronto’s Transportation Services Division to identify a package of interventions to create School Safety Zones. New interventions include physical environment changes, enforcement activities, education and support from a school traffic management facilitator. The Plan is intended to be evidence-based and data-driven. Therefore, it is essential that policy makers and researchers work together to develop appropriate evaluation strategies. Several challenges to policy makers and researchers working together exist; most of which can be overcome using a collaborative process. For example, funding cycles and priorities of granting agencies to fund academic research may not match the timelines and priorities of policy makers. Researchers prefer evidence-based priority setting and random selection to enhance scientific validity, whereas policy makers also consider political priorities and community interests. Although researchers would ideally like to maximize sample size, policy makers often have fiscal restraints. The definition of meaningful and valid outcome measurements is a challenge. Fatal and severe collisions are relatively rare, so proxy measures must be agreed upon prior to the evaluation. Regular meetings of stakeholders will help ensure evaluation that is meaningful to policy makers and scientifically sound. This process will lead to a strategy to be used by City of Toronto, Transportation Services to evaluate the effectiveness of their school zone safety interventions and can provide a model for future evaluations of Vision Zero Road Safety Plan interventions.
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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 | 0.044 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".