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Record W2298517074

Vulnerability, Volunteerism, and Age-Friendly Communities: Placing Rural Northern Communities into Context

2013· article· en· W2298517074 on OpenAlexaffvenueabout
Elaine Wiersma, Rhonda Koster

Bibliographic record

VenueJournal of rural and community development · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsLakehead University
Fundersnot available
KeywordsVulnerability (computing)Context (archaeology)SociologyBurnoutState (computer science)EthnographyEconomic growthRural communitySocioeconomicsPsychologyGeography
DOInot available

Abstract

fetched live from OpenAlex

Volunteerism is considered to be one aspect of age-friendly communities. To be age-friendly is to indicate that the community is committed to promoting the well-being and contributions of older people. Rural northern communities can be vulnerable to external forces, and this vulnerability impacts volunteerism. Using a focused ethnographic approach, the purpose of this study was to examine aging in place in a rural northern community in northern Ontario in a state of economic transition and instability. Interviews were conducted with 84 participants, including older adults, health and social care providers, and other community members. There were several themes emerging associated with the idea of volunteering, including a lack of volunteers, volunteer burnout, community attitudes, a lack of newcomer participation, and a transitory lifestyle. These themes were discussed against the backdrop of a community in a state of economic instability, and reflected the demographic, economic, and social changes that the community had experienced over the last few years. The findings suggest that when examining the aspect of volunteerism within the age-friendly framework, the context of the community has to be taken into consideration.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.277
Teacher spread0.251 · 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 teacher head, not a consensus.

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".

Quick stats

Citations16
Published2013
Admission routes3
Has abstractyes

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