Contemporary sustainable cities: planning healthy, safe, quality and accessible places
Bibliographic record
Abstract
Contemporary cites are changing at an increasingly rapid rate. It is not easy, therefore, to foretell what they will be like even a short time from now, such as in five or ten years (Banerjee, Loukaitou-Sideris, 2011; Carmona, Tiesdell, Heath, Oc, 2010; Carmona, 2014; Friedmann, 2010). \nAccordingly, to achieve sustainable cities today require considering many topics at the same time. In the following, some will be illustrated, including health, safety, quality and accessibility in places and two emblematic examples in Vancouver and Madrid. Since many years, these two cities are working to improve livability connected to health and safety, to achieve both quality and sustainable places and a slow regeneration (Sepe, 2019) which represents one of the main challenges of the future. In line with these concepts, the new needs in teaching will be explored in the conclusions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".