Adapting territories and communities to ageing: public policies and democratic agencies
Bibliographic record
Abstract
Taking older adults into account in the adaptation of territories and communities to population ageing is gaining increasing importance in public debate. Yet what place is really granted to older people in institutions or in the development of public policies? Depending on the context, how are participative approaches being implanted in territories and communities? To respond to these questions, we compare the French and Québec models of adapting to ageing. We start by examining the institutional models used to consult older adults. We then review the application by both models of the international age-friendly cities and communities (AFCC) program initiated by the World Health Organization (WHO). The interest of the program lies in the possibility of establishing an international comparison on the basis of the same participative protocol. Our work reveals the emergence of two forms of democratic organization in the role played by senior citizens in policies to adapt territories to ageing. In France, the central and local levels are disconnecting and the AFC approach is organized relatively autonomously, according to a voluntary sector logic. In Québec, the AFC initiative reflects an integrated approach that connects central and local levels and is taking root in a culture of community development, giving a stronger voice to older adults.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".