The 15-minute City Concept as a Solution to Climate Change in a Regional Context
Bibliographic record
Abstract
The 15-minute city concept is an urban planning strategy that could be used as a solution to climate change in the context of a regional municipality. This paper examines the 15minute city concept as a solution to climate change in York Region. The 15-minute city concept is a popular sustainable urban planning strategy that has arisen out of media that could be useful in creating policies and plans to combat the effects of climate change. My research focuses on the 15-minute city concept as a positive strategy focusing mainly on urbanized cities and neighbourhoods, neglecting the inequalities and problems that could be associated with this concept. I conducted a literature review on regional planning and sustainable urban forms, interviewed planners at local and regional municipalities, examined two example cases, Ottawa, and Paris, and reviewed official plans and policies in York Region and other municipalities. From this research, I have concluded that the 15-minute city concept could work as a solution to climate change in certain parts of York Region. To fully include the 15-minute city concept in York Region, planners would have to create additional site-specific plans and policies. Overall, I think the 15-minute city solution is an accessible concept to understand that could be used in plans and policies to address sprawl and car dependency. Further research could include research on accessible urban planning wording and the 15-minute city viability in other regional municipalities.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".