OLDER ADULTS AS AGENTS OF NEIGHBOURHOOD CHANGE
Bibliographic record
Abstract
Transactional perspectives emphasize that individuals and collectives engage in ongoing relationships with their environments, shaping and being shaped by the places in which they live. Understanding how older adults engage with their neighbourhoods can inform age-friendly practices and policy. This presentation reports on a study that explored ways in which older adults shape their neighbourhoods to support their social engagement, participation and connectedness. We employed an innovative, interdisciplinary methodology combining narrative inquiry, go-along interviews and GPS tracking with 16 older adults living in a medium-sized Canadian city. Analysis suggests that older adults engage in ‘Active Shaping’ and ‘Being Present’, but sometimes experience ‘Powerlessness’ in interactions with their neighbourhoods. ‘Active Shaping’ of the neighbourhood is characterized by inviting social engagement through a variety of strategies, for example, cutting down trees to allow visibility into a porch, greeting people and pets, and patronizing local businesses to support their sustainability as well as interact with others. ‘Being Present’ in a neighbourhood involves few social interactions in the neighbourhood combined with frequent use of local businesses and resources, gaining a sense of familiarity and providing others with a sense of familiarity. ‘Powerlessness’ identified areas where older adults feel unheard, for example, regarding unwanted neighbourhood growth. Study findings suggest that older adults are active agents in their communities and can be forces for change. Findings also highlight the potential to work with older adults to shape neighbourhoods to support inclusion and well-being, and point to areas for advocacy and education of community members.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".