Social Networks in the Aging Process: Geographical Configurations and Related Network Dynamics
Bibliographic record
Abstract
Research on social isolation and loneliness in later life has emphasized family, especially nearby members, as important sources of support. Meanwhile, non-proximate and non-kin relationships have become more prevalent in older adults’ close personal networks. The present dissertation re-evaluates the role of geographic proximity and the retention of non-kin network ties to provide additional insights into older adults’ social connectedness and vulnerability to social isolation. The dissertation consists of three empirical chapters, each using longitudinal survey data from the Survey of Health, Ageing and Retirement in Europe (SHARE), featuring a social network module in selected waves. In the first empirical chapter, latent classes highlight the geographic layouts of older adults’ connections with family members and non-kin ties. Results show that more dispersed geographic configurations have become more common in older Europeans’ core discussion networks. Meanwhile, dispersion does not necessarily translate to lower emotional closeness with network members or decreased network satisfaction. In the second chapter, I implemented a fixed-effect model to scrutinize network losses, namely how one’s relationship and proximity to a lost connection have different impacts on loneliness. I find that network losses in proximity, even for non-kin bonds, are associated with higher loneliness. In contrast, even family losses may not exacerbate loneliness when they happen at a distance. Meanwhile, the introduction of new network members is significantly protective against network losses, including the more consequential losses of family and non-kin confidants in proximity. In the third chapter, I used logistic regression to examine whether individuals in restricted networks consisting only of a partner are less likely than others to bring in additional ties when they experience a network member loss. About a quarter of partnered older Europeans turn out to occupy such partner-exclusive networks, a rate highest among men and childless individuals. Contrary to the expectation that people in such networks are particularly vulnerable to isolation when losing their sole confidant, I find that many manage to recruit additional network members—including connections at relatively far distances. Nevertheless, men and individuals in their 60s are least likely to replenish their networks when experiencing partner losses due to death.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".