374 Implementing healthy urban policy in mid-sized cities in Canada: lessons from Copenhagen
Bibliographic record
Abstract
Background The Cities of Guelph and Oshawa (Durhan Region) are mid-sized cities in Canada that are committed to inclusive active transportation, Vision Zero, and achieving net zero emissions. To facilitate planning to meet these commitments, these cities (together with two others) participated in a study tour/workshop in Copenhagen, Denmark through the Canadian Institutes for Health Research, Healthy Cities Program. The 5-member teams consisted of an academic lead, an elected municipal official, and representatives from public health and transportation. Objective To learn best practices and develop local knowledge mobilization activities in Canadian mid-sized cities to support safe, equitable, and active transportation. Programme Description The teams participated in talks, workshops, and walking and cycling tours with municipal leads, urban planners, architects, public health officials, and community organizations in Copenhagen. The teams then created plans for local activities in Canada which included a neighbourhood walking tour (October 2023) and a community design workshop and panel discussion (Spring 2024) in Guelph, and a Winterize your Bike event (November 2023) and a national hybrid conference (June 2024) in Oshawa. Outcomes and Learnings Learnings included how to design urban spaces that foster inclusive, safe, active transportation, and the importance of relevant data. The knowledge mobilization activities planned by the Guelph team took a local approach including a neighbourhood walking tour of a marginalized area which was attended by 53 participants with wide community representation. The planned design workshop and panel discussion will pull together lessons from Copenhagen with local challenges. The Oshawa team took a national approach, focusing on building collaboration and capacity among similar sized cities. Their 2024 conference will launch a network called the Public, Active, Clean, and Equitable (PACE) mobility group, an apolitical group that can support evidence-informed decision-making; with safety as a central theme. Implications The interactive workshop abroad was invaluable to both researchers and practitioners, and has sparked new collaborations that will support evidence-informed municipal decision-making, policies, and programs. Conclusions Study tours are a meaningful way to learn new approaches and create cross-collaboration within municipalities, and with researchers. These tours are particularly suited to goals to develop healthy urban spaces.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.007 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".