Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The relationship between health and urban design are complex because of the multiple elements, which play different roles in the city system. Indeed, urban happiness, liveability and health are concepts, which in the last years are become always more present in the urban planning studies. Although many theories agree on the benefits that people derive from factors such as green place, quality public space, safe place, social connectedness and clean air, it is not easy to assume and demonstrate that these improve liveability, happiness and then health. Many cities are playing their attractiveness and competitiveness on these elements and current indexes report the ranking of cities which are the most happy, liveable or healthy on the basis of factors in continuous change. In this way is difficult to understand what are the real reasons of success of certain places or cities and what to do to make a city liveable. Although it does not exist a unique recipe, it is possible to identify a mix of ingredients -with the right proportions -which is capable if not of guaranteeing at least strongly contributing to the creation and success of a healthy place. Starting from these premises, aim of this work is to illustrate the more recent theories on healthy and happy places and the original Ecoliv@ble+ design method, carried out in the framework of a CNR research project. The method aims at: identifying sustainable urban health, liveability and happiness from the user's point of view; identifying design interventions to enhance or create these factors. By way of example, the case study of Coal Harbour in Vancouver and relative observation complete the paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it