DEVELOPING AGE-FRIENDLY CITIES AND COMMUNITIES: NEW DIRECTIONS FOR RESEARCH AND POLICY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it