Municipal cross-sectoral policy implementation: urban form and physical activity in Peel Region
Bibliographic record
Abstract
Issue/problem Peel Region is a rapidly-growing, upper-tier municipality in the Greater Toronto Area that consists of predominantly car-oriented urban form. It is also experiencing high rates of physical inactivity and obesity, and the urban form has led to low rates of active transportation. Description of the problem Starting in 2009, our research team began collaborating with the public health and urban planning departments in the Peel municipal government to develop and implement a technical tool that would screen new residential subdivision applications for their impact on creating urban form that is supportive of physical activity. We initially conducted a literature review to determine relevant elements and measures of urban form that promote physical activity and active transportation. The tool was then developed into a Healthy Development Assessment (HDA), which has been undergoing pilot testing since 2013. A recent literature update was conducted in advance of mandatory implementation into the urban planning approval process. Results This presentation will describe the results of technical and feasibility evaluations of the Healthy Development Assessment. The technical evaluation shows that the scores generated by the HDA correctly predict existing urban form and physical activity patterns. The feasibility evaluation shows that completeness and accuracy of HDA materials submitted by urban development applicants has improved, and that overall acceptability of the HDA by the property development industry and local government has improved. Several legislative and other policy barriers still exist however. Lessons It is possible for evidence-based, inter-sectoral municipal policy to be developed and implemented in a way that directs private-sector urban development to build urban forms that support physical activity. There are numerous political, feasibility and policy challenges in doing so, however. Key messages: The evidence base on built environment and health can produce rigorous policy implementation tools for municipal government to regulate land use for healthier populations. Evidence-based, inter-sectoral municipal policy are possible, but face many challenges.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".