Who knows about the origins of the age-friendly cities and communities' topics? A critical analysis of the development process by the World Health Organization and a roadmap for an overhaul
Bibliographic record
Abstract
In 2007, the World Health Organization launched its ground-breaking publication Global Age-friendly Cities: A Guide in which the widely used eight topics of age-friendly cities and communities. A lot of emphasis was put on the self-proclaimed validation using the Vancouver Protocol with older people and other relevant stakeholders. Contrary to popular belief, the eight topics had not been validated until the year 2020. The lack of transparency surrounding the origins of the age-friendly topics, that were rooted in the North-American elder-friendly literature, has led to substantial consequences, including many failed attempts to validate the topics, many difficulties in developing valid measurement instruments, and robust discussion pieces criticizing these topics, for instance, the lack of consideration of financial aspects, as well as safety and security. In this article, the origins of the age-friendly topics are disclosed in an attempt to reconstruct the development process and the choices made by the people involved though a combination of investigative journalism and document analysis approaches. This piece reveals potential pathways and gaps that need to be addressed in the future overhauls of the age-friendly topics that will soon celebrate its 20th anniversary.
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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 | 0.248 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.024 | 0.061 |
| Scholarly communication | 0.028 | 0.048 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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".