Time for My Life Now: Early Boomer Women's Anticipation of Volunteering in Retirement
Bibliographic record
Abstract
PURPOSE: This study explored to what extent early Boomer women who work for pay will be interested in and committed to formal volunteering during retirement. METHOD: Data for this hermeneutic study were gathered through 2 in-depth conversational interviews of 19 English-speaking early Boomer women living in New Brunswick, Canada. RESULTS: Interpretive analysis of interview data revealed that for these early Boomer women, consideration of volunteering in retirement revolves around analyzing the perceived costs and benefits, setting specific criteria for involvement, and recognizing the societal impacts of their refusal to volunteer or their limitation of commitment. IMPLICATIONS: Although not generalizable, the results of this study suggest administrators planning to recruit and retain retired early Boomer women to volunteer should not assume participation at the same rate or with the same commitment as previous generations. New models of volunteer recruitment and deployment may need to be developed to meet the expectations of these women. These participants indicated that formal volunteering will be for personal, not altruistic reasons, on their own terms through direct service; they are not interested in the consuming commitments of board and committee work or fundraising. Volunteering must be meaningful, something about which they are passionate and on their own schedule.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".