COVID-19 and impacts of the evolving city models on mobility
Bibliographic record
Abstract
During the recent pandemic, the progressive weakening of the networks of proximity services for citizens lead to the loss of city functions. It stems the need to define new policy frameworks for the city that puts the user/citizen and urban economies at the center, allowing local regenerative strategies to be implemented and shared between public administrations, business associations, mobility service companies, and citizens. Moreover, recent climate change and the COVID-19 pandemic have highlighted the need to rethink city planning and mobility planning, in particular, ensuring respect for social distancing and supporting the decarbonization strategies dictated by the Green Deal and the Paris Agreement. Local governments can better analyze such critical urban issues from a bottom-up approach through participatory planning. Furthermore, the dissemination of models such as 15-minute and smart cities can ensure that users can reach services with the shortest distance without using a private vehicle. At the same time, the dissemination of technology could allow for greater control of urban activities and transport flows, making it possible to mitigate the impacts of carbon emission and that generated by possible accidents or vehicle congestion. Based on literature review, this study focuses on exploring the diffusion of smart city and 15min city models and the technologies connected to mobility and what that means for the future dynamics of the smart cities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".