‘Payback time’: community volunteering among older adults as a transformative mechanism
Bibliographic record
Abstract
This qualitative study explores the conditions and experiences of older adults' ‘formal’ volunteering through non-profit organisations (NPOs) in Toronto from both organisational and individual perspectives. In spite of the ageing population and the need for NPOs to expand their services, the participation of Canadian seniors in community volunteering has been stagnant for 15 years. What organisational and structural supports might encourage the expansion of volunteering among this group? How do current administrative conditions impact upon senior volunteers? What do older adults expect to gain from community volunteering? The qualitative data collected through interviews, documents and participant observation are analysed using an inter-disciplinary framework that combines theories of the moral economy of ageing, adult development and transformative learning. The results include a socio-demographic profile of senior volunteers in 12 Toronto NPOs, and the administrative characteristics of the six organisations that engage the majority. It is argued that the self-help and transformative mechanisms embedded in community volunteering provide opportunities for retirees to sustain their self-esteem and sense of wellbeing, while cultivating ‘generativity’ in late adulthood. Promoting transformative learning enables community volunteering to provide meaningful roles for seniors, and promotes citizenship participation and the social economy in an ageing society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".