Active aging and urban policies: the space as an instrument for an inclusive and sustainable city
Bibliographic record
Abstract
Aging is one of the most important challenges of our times. As stated by United Nations' report on 1983, "Policies to meet the challenge of a growing, healthier and more active seniors population -based on the view of the ageing of society as an opportunity to be utilized -automatically benefit the individual ageing person, materially and otherwise. Similarly, any effort to ameliorate the quality of life for the seniors, and to meet their diverse social and cultural needs, enhances their capacity to continue interacting with society". Aging society provides not only a new context, but a new opportunity to rethink our traditional views of age. The growing number of seniors people will soon make the majority of overall population. As noticed by reports of international organizations, cities will play a more important role in dealing with these quantitative and cultural changes, mostly because it is expected that a quarter of the population over 60 will be concentrating in the central areas of compact cities. In brief, cities are at once growing and aging at an incredible speed. Although aging process represents a fundamental and structural phenomenon with very deep consequences at economic, social and political level, and with an impact on the individual one as on the society as whole, our cities should deal with this process and respond, in terms of public health and social care, to needs of older people, also those that will experience a loss of autonomy. It is important to note that there are a greater heterogeneity within older population in terms of conditions and demands, which depend on their specific personal, social and familial context. Urban space - in its complex differentiation between public space, third space, and private space - represents both a tool and a strategic factor in pursuing the objective of ensuring high levels of widespread well-being and, from a political perspective, fully shaping the right to the city for seniors individuals.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".