Co-Creating Sustainable Age-Friendly Communities: Civic Engagement in the Age-Friendly Niagara Movement
Bibliographic record
Abstract
Since the World Health Organization (WHO) launched its global network for age-friendly cities (AFC) movement in 2010, the number of participating cities and towns, as well as the body of literature focusing on this initiative has grown steadily. Nevertheless, few studies have directly examined how older adult volunteers are involved in AFC planning and initiatives for their municipalities. This study explores the experience of citizen volunteers, mostly older adults, engaging in local municipal-level age-friendly (AF) advisory committees as a part of the Age-Friendly Niagara (AFN) movement in Ontario, Canada. Since its conception as a grassroots movement in 2013, the AFN Network (AFNN) has expanded across the entire region, as each municipal government has appointed its local AF advisory committee or an equivalent, which consists of citizen volunteers, at least one councilor and one municipal staff member. Employing a qualitative multisite case study approach, we conducted focus groups with eight municipal AF advisory committees (or their equivalent) (n = 48, average age 69) to explore their roles, achievements and challenges. Our findings highlight the crucial role older adult volunteers play in their local AFC initiatives as they strive to co-produce and co-create sustainable age-friendly communities in collaboration with their municipal government.
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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 | 0.003 | 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 | 0.016 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".