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Record W4239423526 · doi:10.1017/s0144686x21000477

Cultural generativity in perspective: motivations of older Jewish volunteers

2021· article· en· W4239423526 on OpenAlexaff
Eireann O’Dea, Andrew Wister, Sarah L. Canham

Bibliographic record

VenueAgeing and Society · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGenerativityJudaismPerspective (graphical)PsychologyQualitative researchSocial psychologyFaithDutySociologySocial science

Abstract

fetched live from OpenAlex

Abstract The physical, mental and social benefits for older adults who volunteer are well-documented. Absent from this area of research is an understanding of volunteer motivations among ethnoculturally diverse older adults. This paper addresses this research gap by examining motivations to volunteer related to cultural generativity among Jewish older adults, a group that remains underexplored in research. Cultural generativity is defined as an impulse to pass down one's culture to the next generation, and thus to outlive the self. The Jewish community is notable for possessing high levels of social capital, indicated by close community ties and the large number of faith and culturally based organisations, and therefore makes them an important ethnocultural group to study. Semi-structured qualitative interviews were conducted with 20 adult volunteers age 65 and over. The guiding research questions for this study are: What are the motivations to volunteer among older Jewish adults? and Do these motivations align with the concept of generativity applied to Jewish culture? Data analysis identified three themes related to cultural generativity: volunteering to preserve and pass down Jewish traditions and teachings; a Jewish ethic of giving back perceived as a duty; and experiences of anti-Semitism and discrimination motivating Jewish participants to volunteer. Findings suggest the ways in which cultural generativity may be expressed through volunteerism.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.352
Teacher spread0.322 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2021
Admission routes1
Has abstractyes

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