Enacting agency: exploring how older adults shape their neighbourhoods
Bibliographic record
Abstract
Abstract Within research on ageing in neighbourhoods, older adults are often positioned as impacted by neighbourhood features; their impact on neighbourhoods is less often considered. Drawing on a study exploring how person and place transact to shape older adults’ social connectedness, inclusion and engagement in neighbourhoods, this paper explores how older adults take action in efforts to create neighbourhoods that meet individual and collective needs and wants. We drew on ethnographic and community-based participatory approaches and employed qualitative and geospatial methods with 14 older adults in two neighbourhoods. Analysis identified three themes that described the ways that older adults enact agency at the neighbourhood level: being present and inviting casual social interaction, helping others and taking community action. The participants appeared to contribute to a collective sense of connectedness and creation of social spaces doing everyday neighbourhood activities and interacting with others. Shared territories in which others were present seemed to support such interactions. Participants also helped others in a variety of ways, often relating to gaps in services and support, becoming neighbourhood-based supports for other seniors. Finally, participants contributed to change at the community level, such as engaging politically, patronising local businesses and making improvements in public places. Study findings suggest the potential benefits of collaborating with older adults to create and maintain liveable neighbourhoods.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".