Approximation to the Concept of 15mC in the Historic Centre of Valencia (Spain): Demographics, Economy, Housing and Emblematic Establishments
Bibliographic record
Abstract
In the last decade, but especially since the COVID-19 pandemic, several cities around the world such as Paris, Melbourne, Shanghai and Ottawa or Madrid and Barcelona in Spain, are adopting urban replanning models to offer basic services to residents within a maximum radius of 15 minutes. This model of smart city, recently launched by Franco-Colombian urban planner Carlos Moreno, advocates a polycentric, multi-service and functional city in which every citizen can access the six priority urban functions to guarantee their quality of life: housing, work, local commerce, healthcare, sociability and public space. Ensuring these urban functions also allows us to move closer to achieving the SDGs of the 2030 Agenda. The research presented here addresses the study of Valencia and in particular its historic centre from the perspective of a smart city or 15-minute city. The first part presents the methodology used to analyse whether or not these functions for urban life are fulfilled based on the most recent publications. Secondly, the databases offered by the Statistics Office of the Valencia City Council are analysed and interpreted to compare the data obtained with some existing works that grant the city of Valencia a fulfillment of more than 90%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".