Cultural generativity in perspective: motivations of older Jewish volunteers
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".