THE CO-EVOLUTION OF PERSONAL NETWORKS AND LONELINESS FOLLOWING WIDOWHOOD FOR MEN AND WOMEN
Bibliographic record
Abstract
Abstract Late-life transitions such as widowhood often re-calibrate people’s close personal networks. Some network change—particularly growth in network size and closer geographic access to network members—is assumed to protect against loneliness, but scholars have yet to systematically examine these processes post widowhood. This study uses data from four waves of the German Aging Survey (DEAS), conducting gender-specific hybrid panel modeling to estimate both within- and between-individual effects of (1) network conditions up to seven years past widowhood, and (2) the effects of network change on loneliness. Results reveal that network size takes on a reversed U-shape: Germans becoming widowed tend to see an influx of new core ties from beyond their family, but this trend slows and reverses with time. There was also some evidence of non-linear change related to distance: geographic distance to core network members tended to shrink following widowhood before expanding back outward. Furthermore, moderation analyses an important role for these network characteristics on loneliness. Larger non-kin networks in the aftermath of widowhood partially alleviated the loneliness associated with that transition. The role of geographic proximity was gender-specific, as widowed men with nearby non-kin ties were most protected against loneliness, whereas widowed women fared best if their kin ties were farther away. Altogether, this study presents novel insight into how personal networks evolve after widowhood, revealing the nuanced, gendered ways that networks adapt. Efforts to reduce loneliness after widowhood may consider how gender roles and expectations shape the transmission and meaning of companionship and support.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".