Urban impacts on aging: what can we learn from the “Age-Friendly city” methodology?
Bibliographic record
Abstract
People are aware that environment affects their well-being, however few of them reflect about these questions, and even less manifest their impressions and public claims efficiently in order to benefit the community. Urban planning is related to diverse disciplines that compose environmental gerontology, nonetheless it rarely considers reports from the experiences of the elderly and professionals who assist them. Urbanization has its pros and cons. On one hand, it can create an increase of human interactions, yet on the other hand the environmental negative effects can hinder these meetings, creating distances and transforming the social public life. The World Health Organization’s “Age-friendly cities” methodology introduces guidelines applicable to different geopolitical contexts. It constitutes an opportunity to know the impressions of those who live or work in the studied area, offering important insights for government action. It can also grant legitimacy for the process, since it would be based on the Vancouver Protocol through scientific research. The greatest contribution is offering a voice for the citizens to express their perceptions and requirements regarding the city, giving value to their opinions. Certainly, it generates a more democratic and authentic way of exercising citizenship, making human rights more effective.
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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 | 0.090 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".