DEVELOPING AGE-FRIENDLY CITIES AND COMMUNITIES: NEW DIRECTIONS FOR RESEARCH AND POLICY
Bibliographic record
Abstract
Developing what has been termed ‘age-friendly cities and communities’ (AFCC) has become an important area of work in the field of public policy and ageing. This reflects the increasing importance of older people within urban as well as rural communities; the importance of the physical and social environment for maintaining quality of life; and the emphasis in community care policies on promoting ‘ageing in place’. This symposium will provide an assessment of a range of initiatives underway to develop age-friendly communities, drawing upon examples from Europe and North America. An-Sofie Smetcoran and colleagues address how age-friendly social environments can support frail older people to ‘age actively in place’. Their discussion highlights that this approach could be particularly beneficial to those who lack the means to improve their situation and to those more reliant on their immediate locality for support, providing improved prospects for ‘ageing well in place’. Samuele Remillard Boillard examines age-friendly activity in Brussels, Manchester and Montreal, providing a critical overview of the success factors and challenges influencing the development and evolution of policies in these cities. Kieran Walsh and Anna Urbaniak review findings from a project exploring the impact of critical life transitions on experiences of old-age exclusion, and the role of place and community in mediating these experiences. Finally, Tine Buffel and Chris Phillipson will conclude the symposium by outlining a ‘Manifesto for the Age-Friendly Movement’, focusing on issues around: challenging social inequality; widening participation; coproducing age-friendly communities; and integrating research with policy.
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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 | 0.060 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.033 | 0.081 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.030 | 0.021 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".