Age-Friendly Approaches and Old-Age Exclusion: A Cross-City Analysis
Bibliographic record
Abstract
Developing ‘Age-Friendly Cities and Communities (AFCC)’ has become a key part of policies aimed at improving the quality of life of older people in urban areas. Despite this development, there is evidence of rising inequalities among urban elders, and little known about the potential and limitations of the age-friendly model to reduce old-age exclusion. This article addresses this research gap by comparing how Brussels, Dublin, and Manchester, as three members of the Global Network of AFCC, have responded to social exclusion in later life. The article combines data from document analysis and stakeholder interviews to examine: first, the age-friendly approach and the goal of reducing social exclusion; and second, barriers to developing age-friendly policies as a means of addressing exclusion. The paper suggests that there are reciprocal benefits in linking age-friendly and social exclusion agendas for producing new ways of combatting unequal experiences of ageing in cities.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".