Epistemology and methodology of participatory research with older adults
Bibliographic record
Abstract
Promoted by the World Health Organization, Age-friendly Cities and Communities (AFCC) projects are multiplying, presenting variations related to national contexts and methodologies put in place to support the involvement of older adults. Participation is modulated according to local policies, environments, methods of participation, profiles of older adults, local actors, etc. Considering the aim of social inclusion, participatory process, empowerment, and collaborative partnership, many differences exist between AFCC projects. This chapter proposes a comparative dialogue on participatory methods of AFCC with an emphasis on their challenges, effects, strengths, and limits. In this perspective, four AFCC case studies crossing different French-speaking contexts of Europe and Canada will be presented and analyzed. The first one is about older adults’ participation within the management of cities (Quebec, Canada). The second concerns older adults of a rural territory, participatory practices, and inclusive process (France). The third case observes a French minority community and the challenges of mobilizing social actors on a community housing project for older adults (New Brunswick, Canada). The fourth analyzes the role of committed actors and the potential for social change through the empowerment of older adults (Wallonia, Belgium).
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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 | 0.151 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.009 | 0.077 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".