Epistemology and Methodology of Participatory Research with Older Adults: A Comparison of Four Age-friendly City National Experiences
Bibliographic record
Abstract
The aim of this chapter is to present a comparative approach on participatory methods within four AFCC case studies from different French-speaking contexts.1 First, the Université de Sherbrooke in Quebec (Canada) is interested in observing older adults’ participation within the management of cities and the older adults’ organizations involved in AFCC. Second, the Université catholique de Lille (France) leads a research that consists in supporting older adults of a rural territory to analyze by themselves the different dimensions of their own participatory practices, in order to reinforce collective reflexivity and inclusive process. Third, at the Université de Moncton, in New Brunswick (Canada), participatory research seeks to help a French minority community implement an AFCC’s action on community housing for older adults and, in doing so, highlights the challenges of rallying social actors and older adults in a knowledge mobilization process. Fourth, the Université catholique de Louvain in Belgium, through the Age-friendly Wallonia project, challenges the development of a collective conscience and the potential for social change through the empowerment of older adults. Then, a researcher from the Université Grenoble Alpes (France) examines the relationships between these AFCC experiences to highlight common challenges and opportunities through such a rare international comparison.
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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 | 0.037 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".