Exercising senior citizenship in an ageist society through participatory action research: A critical occupational perspective
Bibliographic record
Abstract
This paper analyses the experiences of a group of senior citizens living in a large Canadian city as they engaged in advocacy focused on home care services. The methodology used was participatory action research (PAR); data were collected over a 2-year period. Findings are considered within an understanding of citizenship as an occupational role, an occupational possibility constrained by conventional ageist views on ageing as reinforced and informed by successful ageing concepts situated within a neo-liberal political context. Key themes emerging from the analysis were naming, exposing and resisting ageism; identifying oneself as a senior citizen; balancing occupational demands in light of age-related changes; and social media as an exclusionary or enabling tool for advocacy. The PAR project is described as it was experienced around several political social actions including letter writing campaigns, political dispositions, and the development of informational materials on ageism. As a socio-cultural condition constraining occupational possibilities for older adults, findings highlight how ageism shapes how senior citizens exercise their citizenship through resistance to normalizing influences. The study illustrates a transformative approach to occupational science research aimed at creating knowledge that leads to social change.
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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 | 0.032 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.042 | 0.074 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| 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".