Age, Retirement, and Health as Factors in Volunteering in Later Life
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Volunteering in later life attracts attention because its benefits older volunteers, voluntary associations, and society. Unfortunately, researchers and practitioners struggle with the complexity of predicting who volunteers. The authors ask whether a rough identification of older volunteers solely based on age is possible. The authors answer this question by means of structural equation modeling, analyzing international survey data. The findings show that the direct effect of age on the time older people spend volunteering is negligible. Moreover, the age patterns in volunteering created by retirement and declining health are weak. Those findings make age an unsuitable indicator for volunteering in later life. The authors recommend that voluntary organizations and policy makers use personal characteristics, such as health status, when defining their target groups for programs that encourage volunteering. In addition, researchers should not use an age group when referring to the third age, meaning the active and productive part of old age.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it