Developing a 15-minute city policy? Understanding differences between policies and physical barriers
Bibliographic record
Abstract
The concept of a x-minute (or 15-minute) city has recently gained prominence as an influential urban planning approach. Recent research showed how American, Canadian, and Australian cities operationalized the concept differently using diverse temporal cut-off values and types of destinations. Despite this, there has been little effort to understand how different 15-minute city policies are comparable, and to what extent physical elements in cities can affect realizing the concept. To address this gap, this study aims to understand parallels and differences between these policies while understanding the impacts of the city’s structuring elements on the probability of achieving them. Using a wide array of spatial and transportation data for the City of Saskatoon, the paper develops five different 15-minute city policies based on four different city plans at the parcel level. Using summary statistics and multilevel logistic regressions previous policies were analyzed. The study shows considerable differences between policies in terms of the conclusions they convey. For example, different policies led to diverse results regarding their relationship with people’s socioeconomic issues, and thereby equity assessment. Additionally, the study shows that some physical elements such as highways, large parks, and rail lines have a consistent negative impact on the probability of realizing 15-minute city goals, regardless of the used policy. Other elements had a mixed effect according to the used policy. This study offers cities a better understanding of the performance of different 15-minute city policies and the relative challenges in realizing them, helping cities achieve their broader sustainability and equity goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".