The Co-Evolution of Personal Networks and Loneliness Following Widowhood: Resources or Costs for Older Men and Women?
Bibliographic record
Abstract
Drawing from the life-course framework and an integrated model of social resources and social costs, this article investigates (1) the dynamics of personal network changes, (2) their impact on loneliness following widowhood, and (3) the gender-specific effects of these changes. Analyzing panel data from the German Ageing Survey ( N = 7,012; observations = 20,816) using multi-level mixed-effects models, the study reveals a modest expansion in non-kin networks and the number of children in networks after widowhood. Additionally, the findings indicate that over time, widowed individuals generally experience a reduction in the distance to their nearest network members, particularly kin. Growing non-kin networks are associated with lower loneliness following widowhood. Geographic changes in networks display gender-specific patterns: proximity to children is linked to reduced loneliness for widowed men, but greater loneliness for widowed women. These results underscore the complex and gendered nature of relational adaptations to widowhood, highlighting that network changes can offer both benefits and challenges during life transitions. The study also suggests that considering opportunity costs can be a valuable extension of the social cost framework.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".