Isolation or Replenishment? The Case of Partner Network Exclusivity and Partner Loss in Later Life
Bibliographic record
Abstract
OBJECTIVES: People's partners and spouses often provide a wide range of essential emotional and practical support. As crucial as they may be, a nontrivial segment of the older population appears to limit close discussions to their partner alone, a phenomenon we term "partner network exclusivity." This network structure could leave people vulnerable to partner losses and subsequent social isolation. The present research has 3 aims: (a) examine the prevalence of partner-exclusive networks among European older adults; (b) consider who is most likely to inhabit such networks; and (c) investigate whether and how individuals in such precarious networks rebalance them in case of partner losses. METHODS: The analysis uses Wave 4 (2011) and Wave 6 (2015) of the Survey of Health, Ageing and Retirement in Europe (SHARE) to perform logistic regression on one's possession of partner-exclusive networks and the addition of core ties. RESULTS: More than a quarter of partnered respondents (28.1%) are in partner-exclusive core networks. Men, childless individuals, and those with financial difficulties are most likely to occupy such networks. Individuals in partner exclusivity are especially likely to enlist additional ties upon partner loss. Nevertheless, men and individuals at early old age are relatively unlikely to rebalance their core networks in case of partner death. DISCUSSION: This study provides new evidence that network replenishment following relationship disruptions is plausible even for those from precarious network settings. Nevertheless, widowhood produces patterns of vulnerability for a subset of older adults in partner-exclusive core networks.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".