Ageing in Space: Remaking Community for Older Adults
Bibliographic record
Abstract
In this paper, we explore the needs of older adults for social interaction by investigating how local and everyday communities are produced by service organisations and experienced by their patrons. We approach the social needs of older adults through the lens of ‘community,’ both as a concept and as a lived experience. Our attention to communities of peers and arenas for everyday interaction is discussed in the context of the dominant policy discourse of ‘ageing in place.’ In this discourse, ‘place’ is predominantly interpreted as physical infrastructure, with little formal recognition of the importance of the arenas of social everyday interaction for older adults outside the home/family.Our exploration draws on the empirical study of three organisations in Toronto, Canada and Bergen, Norway that, in various ways, represent places for everyday interaction. We discuss how belonging is understood from the perspective of different older groups and how it is facilitated by organisations and services, through the creation of shared, informal social spaces. Even though there is considerable difference in size, aesthetics, target population and geographical impact field, all three organisations offered their patrons a space for informal social interaction in which they were allowed to claim the space as their own. Our analysis indicates a pronounced need for a diversity of arenas for older adults to interact socially. Furthermore, we portray how these spaces for everyday interaction are created often in addition to, or even in divergence from, the official mission of these organisations, in a form of co-optation by patrons.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".