PW 0757 Vision zero in canada: building multi-sectoral capacity for implementation
Bibliographic record
Abstract
Canada’s Road Safety Strategy 2025 sets Vision Zero (VZ) as a goal for the future, with the onus on individual jurisdictions for action. With no clear guidelines, recommendations, or frameworks, road safety professionals and individual jurisdictions wanting to implement VZ are seeking evidence-based information, resources, and experts. The objective was to meet the demand by creating one-stop access to the VZ framework, addressing questions and concerns from road safety stakeholders, and synthesize evidence-based resources to increase capacity. Parachute solicited feedback from stakeholders across Canada, inquiring about the gaps of VZ implementation. Stakeholders wanted access to: information on the VZ concept; examples of implementation in other jurisdictions; leaders in health, traffic engineering, police enforcement, policy, and advocacy; and evidence-based strategies ranging from speed reduction, road design, and policy changes. In response, the Parachute VZ network was created (modeled after the successful U.S. Vision Zero Network) and complemented two national conferences bringing together more than 350 delegates from across Canada. Since the May 2017 launch, Parachute’s VZ network has become Canada’s leading voice on VZ. Parachute convenes 250 road safety stakeholders through multiple platforms and provides national leadership through the synthesis of evidence and experience. Resources such as the use of data to drive decisions and satisfy Complete Streets in both rural and urban settings, current research, links, communication campaigns, tools, and frameworks are now available in one place. The network is a commitment that helps mobilize Canadians to change how we think about road safety, bringing together multi-disciplinary stakeholders and building the capacity to ensure the right to safety for all.
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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 | 0.049 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".