Who is living a local lifestyle? Towards a better understanding of the 15-minute-city and 30-minute-city concepts from a behavioural perspective in Montréal, Canada
Bibliographic record
Abstract
Policy makers worldwide are increasingly embracing the idea of a “15-Minute City” or “30-Minute City” as part of their sustainable-development strategies. These planning concepts propose an urban environment where residents can meet their essential needs within a short trip from their home using active modes of travel. However, there is limited understanding about the replicability and usefulness of these concepts in influencing the travel behaviour of residents to meet the 15- or 30-minute-city reality. Drawing from a travel-behaviour survey and open-source geospatial data from Montréal, Canada, this article seeks to identify which groups of households are living a 15- or 30-minute city lifestyle to understand the compatibility of the x-minute city planning approach with the local North American context. Findings indicate that the 15- and 30-minute city paradigms provide goals that are hardly reachable in the context of a large North American city. Very few households are able to conduct all their daily travel within close proximity to their home, even if the built environment was substantially altered. These findings suggest that the x-minute city is not a one-size-fits-all model. The findings from this study can be of interest to transport professionals aiming to apply the x-minute city as it highlights the challenges associated to meeting such target in a North American context.
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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 | 0.004 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".