The UK Network of Age-friendly Communities: a general review
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a detailed account of the work and contribution of the UK Network of Age-friendly Communities, a platform established to support the development of age-friendly communities across the UK. Design/methodology/approach This paper draws on a review of both external and internal working documents, communications with network representatives, and an in-depth interview conducted with the current manager of the UK Network of Age-friendly Communities. Findings Since its formation, the UK Network of Age-friendly Communities has provided cities with an important platform for knowledge exchange and peer support, and helped build commitment to the age-friendly agenda at the local, national and international level. Through the presentation of various examples, the article illustrates that network members have not only helped drive this agenda forward by developing a collective voice, but also by developing a wide range of initiatives at the local level. Originality/value Despite an increased interest in documenting age-friendly experiences around the world, the experience of national programmes remains under-explored in the age-friendly literature to date. To the knowledge, this paper is one of the first to describe the work and contribution of the UK Network of Age-friendly Communities.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".