Bibliographic record
Abstract
The world's population is ageing at unprecedented rates. Given the growth of the older adult population, it is not surprising that governments and policy makers in many regions throughout the world have been turning their attention to the implications of population ageing on social and economic development. More specifically, there has been much concern about the consequences of an ageing population on health care systems and costs and an emphasis on finding ways to help older adults age well. Leisure can play an important role in the ageing well process but, to a large extent, its role in healthy ageing is often overlooked by policy makers. This paper focuses on leisure in later life, particularly as it is related to notions of healthy ageing and ageing well. It summarises what we currently know about the role of leisure in later life. Although the relationship between leisure and ageing well is complex, the existing evidence is clear that leisure can provide meaningful opportunities for continued engagement in life—for being, becoming, and belonging (Renwick & Brown, 1996)—and is essential for ageing well. However, not all have equal access to leisure in later life, which can threaten the well-being of those who are marginalised in society. The paper also identifies some of the gaps in our understanding and concludes with a list of recommendations for future research on the role leisure can play in the ageing well process.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".