WHO Age Friendly Cities: Enacting Societal Transformation through Enabling Occupation
Bibliographic record
Abstract
Age-friendly cities and communities (AFC) is an international movement initiated by the World Health Organization in response to simultaneous patterns of global aging and urbanization. A key aspect of AFC is a commitment to a cycle of continual improvement that addresses key aspects of the environment, such as accessibility, transport, intergenerational links, respect, community participation, and service provision. Given the focus of environmental influences on activity participation, this policy initiative overlaps with the core domain of concern of occupational science, that is, occupation. This discussion paper contends that occupational science is aligned with principles of active ageing and AFCs, and has the potential to provide evidence for the link between occupation and health, and open novel ways to think about and attend to the occupational rights of ageing persons. The frameworks of active ageing and AFC provide a means for occupational science to enact its values on enabling occupation and related concepts. An occupational science perspective can emphasize the development of enabling policies and, combined with the principles of AFC, contribute to the development of social policies and their enactment within local contexts, based upon a complex understanding of occupation and its relationship to health and well-being.
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.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.004 |
| 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".