Bibliographic record
Abstract
Population ageing has created an urgent need for societies to think strategically about what constitutes a good old age for its citizen--and to formulate actions toward achieving this goal. We present a template for global action that requires challenging cultural norms of old age as an inevitable period of decline; and fostering supportive environments to enable wellbeing. Keating sets parameters around thinking and acting differently: focusing on physical, social and policy environments and on the importance of creating congruence between resources of older persons and these environments. Beard presents global patterns of ageing, emphasizing that global responses require understanding how these patterns are expressed across world regions. He speaks to strategic objectives adopted by WHO and its country constituents to embrace a commitment to action on healthy ageing and to develop age-friendly environments to address the multiple and intersectoral influences that affect quality of life in older age.
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 | 0.039 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.056 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 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".