EXPLORING GENERATIVITY AMONG CULTURALLY DIVERSE OLDER ADULT VOLUNTEERS
Bibliographic record
Abstract
Abstract Generativity, the desire to look beyond the self, and teach and guide the next generation, has been described by researchers as an important component of successful aging and a key part of why older adults choose to engage in activities such as volunteering. Recent research on generativity, including Rubinstein et al’s generativity framework (2015), describes how the expression of generativity can be influenced by cultural context, including traditions, sense of heritage, and family relationships. Despite this, so far little research has focused on generative expression among older adults with diverse cultural backgrounds, such as those belonging to ethnocultural minorities. Using Rubinstein’s framework and life history interview as a guide, this study explores generativity among older adult volunteers who belong to ethnocultural minority communities in Vancouver, British Columbia, Canada. 14 participants aged 65 and over participated in in-depth interviews about various life course experiences, in order to bring contextual understanding to their generativity and volunteer activities in later life. Preliminary findings suggest the unique ways in which generative action can develop over the life course, and how it can potentially be influenced by a sense of belonging to an ethnocultural group. Results also speak to the contributions that are made by ethnocultural minority older adults as volunteers in their communities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| 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".