Assessment of a urban sustainability and life quality index for elderly
Bibliographic record
Abstract
The research here presented originates from some of the ongoing challenges of our society: the demographic changes and the high share of population living in urban areas.The aim of the research is the definition of an index of sustainability and quality of life for elderly at suburban scale, not only able to outline aspects related to the local territorial context but also to represent the neighbourhood level.This would provide significant added value to existing indicators that represent the quality of life for much larger areas as cities, regions and nations.Indeed, it is universally recognised that the quality of life varies quite considerably depending on whether you live in different neighbourhoods or urban areas.The index proposed with the research is elaborated on the base of objective and subjective indicators integrated with the judgment of experts, in order to give an accurate and truthful weight to each indicator.Moreover, it has been elaborated for microscale analysis, dividing urban areas into cells, in order to highlight more in detail the real needs of each specific zone.As a result, the elaborated index would be an available tool to be provided to the local public administration to facilitate and optimise the urban planning and management, prioritizing interventions on the base of the sustainability principles and subjective needs of the population, in particular of elderly.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".