PRESIDENTIAL SYMPOSIUM: AGE-FRIENDLY ENVIRONMENTS: CRITICAL DISCUSSIONS ON PRESENT PRACTICES AND FUTURE PATHWAYS
Bibliographic record
Abstract
Age-Friendly Cities and Communities (AFCC) and Age-Friendly Environments (AFE) initiatives and practices offer significant potential for improving social inclusion, health and wellbeing of older people worldwide. With the support of the World Health Organization, they present an experimental landscape for municipalities to adapt physical and social environments and a platform for researchers to discuss age friendliness. A. Scharlach presents the complexities and controversies regarding the concept of age friendliness and its implications, including potential benefits and limitations of an emphasis on individual health and functional ability, as embodied in WHO’s 2015 World Report on Ageing and Health as opposed to social inclusion and community well-being. AFCC and AFE initiatives have expanded worldwide. However, little is known about their effects, their embeddedness in existing policies and their sustainability, or how best to adapt to local needs. Meeting these challenges, S. Garon and colleagues present data from Quebec and use three theories of evaluation (experimental, logic model, participatory) adapted to distinct variable contexts. At a global level, A. Ross similarly exposes the need to critically consider such contexts as a key factor in adapting a global WHO monitoring framework and core indicators to measuring age-friendliness of places. With a focus on dementia, S. Biggs and I. Haapala offer a complementary view on the competing narratives at stake within age friendliness in Australia. In conclusion, T. Moulaert uses comparative material from Quebec, France and Belgium to advocate for the need for theory to understand local mediations and how they are embedded in shared values, language and interests.
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.027 | 0.053 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".