Growing old in resource communities: Exploring the links among voluntarism, aging, and community development
Bibliographic record
Abstract
Abstract This article addresses the implications of “resource frontier aging” with a particular emphasis on understanding the role of the voluntary sector in supporting older people living in resource hinterland regions, and in sustaining economic and social development in their communities. Informed by recent calls for greater attention to the diverse experiences of rural aging, individually and collectively, and drawing on previous research on voluntarism in aging rural communities and on community development in Canada's resource hinterland, we conceptualize voluntarism as a multifaceted process that both shapes and is shaped by the interactions between older people and aging places. We articulate an approach for interrogating initiatives that link voluntarism, aging, and community development together, and present findings from an exploratory analysis of local media from two resource towns in northern British Columbia to identify and describe the interrelationship between voluntary sector activities in support of community development and those in support of aging in place. The findings provide evidence of the multiple, nuanced, and reciprocal ways in which voluntarism creates and reinforces the connections between aging and community development, often via the leadership and participation of older residents. Avenues for further research aimed at developing an in‐depth understanding of voluntarism as a potentially transformative process in resource frontier communities are discussed.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".