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Record W2985848887 · doi:10.1093/geroni/igz038.064

RURAL LIBRARIES AS CONTESTED SPACES OF OLDER VOLUNTARISM IN AGING RURAL COMMUNITIES

2019· article· en· W2985848887 on OpenAlexaffabout
Amber Colibaba, Mark W. Skinner

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsTrent University
Fundersnot available
KeywordsVoluntarism (philosophy)Rural areaPopulation ageingEconomic growthPolitical scienceSociologyGerontologyPopulationMedicine

Abstract

fetched live from OpenAlex

Abstract Recent efforts to better understand voluntarism as fundamental to how rural communities are meeting the challenges of population ageing have highlighted ageing rural volunteers, and the attendant burden of older voluntarism, as key issues for ageing in place of rural residents and ageing rural community sustainability. Drawing on a case study of a volunteer-based rural library in Ontario, Canada, this study examines the experiences of older volunteers, the challenges of sustaining volunteer programs, and the implications of older voluntarism for rural community development. Findings from interviews and focus groups with library volunteers, staff, board members and community stakeholders demonstrate how the experiences of older volunteers and challenges of older voluntarism affect rural community development. The results reveal how participation, well-being, conflict and territoriality associated with older voluntarism contributes to ‘contested spaces of older voluntarism’ whereby older volunteers negotiate their rights and responsibilities associated with ageing and volunteering in rural communities.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0210.021
Scholarly communication0.0120.005
Open science0.0020.018
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.028
GPT teacher head0.303
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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