Proximity planning for healthier cities: lessons from Barcelona, Bergamo, Ottawa, and Portland
Bibliographic record
Abstract
Healthy cities and proximity urban models have become rising trends in the planning discussion. Urban environments that foster walkability, green infrastructures and intergenerational spaces play a key role in developing healthier cities. Urban proximity strategies, such as Paris’ 15-minute city, are now renowned for aiming to have essential daily services close to home. However, they came from long-standing urban traditions such as the Neighbourhood Unit in the U.S. or Time Planning in Italy or Germany. This study explores how four cities, Barcelona, Bergamo, Ottawa and Portland that showcase some of these traditions, integrate proximity and urban health into their planning policies. Despite the diversity of scales, approaches and tools adopted (regional, territorial or urban; land use, health or mobility; plans, regulations or recommendations), some common principles arose that yield a more purposeful vision of proximity. It includes improving urban communication, promoting sustainable transport, fostering intergenerational inclusion, and developing green and pedestrian infrastructures. The article underscores the fact that, notwithstanding the different strategies, the four cities are successfully integrating proximity and health into their city plans. The presented discussion provides a framework to guide these kinds of efforts in future cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".