Bibliographic record
Abstract
Current needs of people are continuously changing due to the rapid transformation of territories, which present more and more social, virtual, environmental and urban infrastructures, which are intersected and overlapped in different and not always sustainable manner. However, some points seem to be important for the well-being of people and sustainability of places. For these reasons, the needs of more healthy, happy and liveable places are increasing, and the studies on these fields are becoming always more important to identify both the intangible and tangible aspects capable of giving a scientific point of view on the above topics. If from a part many indexes have been created, from the other these change continuously and are created with different parameters, which can sometimes give rise to a nonunivocal interpretation. Furthermore, many studies are focused only on one aspect capable of giving health, happiness and liveability and do not consider the intangible aspects suitably. The most happiest city or the most liveable place or, again, the city which is considered the healthiest are data which are more and more used to increase attractiveness and competitiveness to an area of transformation or a whole city. The use of a correct method to collect and use these data suitably is currently a need to obtain a sustainability meant in the threefold meaning, namely, social, environmental and economic one. Starting from these premises, the aim of this study is to present the main research on these topics and illustrate the original Ecoliv@ble+ design method, which was created in order to identify urban health, liveability and happiness from the users' point of view and identify sustainable design interventions to enhance or create these factors. The emblematic case of False Creek area in Vancouver, British Columbia, interested by a long process of urban regeneration, and relative observation on the method conclude the article.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".