Satisfaction with Retirement: A Qualitative Comparative Analysis with Social Network Analysis
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
Abstract Satisfaction with any aspect of life is not easy to defined, and sometimes, it is still a topic of discussion. That is especially relevant for more excluded populations like older people. This research looked into how relevant the social support networks (SSNs) of older people are for their satisfaction with retirement, specifically in the Chilean context. It will identify some sufficient and necessary conditions for older people to be satisfied with retirement. This research focuses on 30 life histories of older people in Santiago, Chile. They were asked about their histories and SSNs. The analysis applied used a Qualitative Comparative Analysis (QCA) with conditions from the Social Network Analysis (SNA). The results identify sufficient and necessary conditions to achieve satisfaction with retirement. It is highlighted some of the dimensions of SSNs and their reciprocities as relevant conditions for satisfaction with retirement.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.041 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it