Older Adults’ Descendants and Family Networks in the Context of Global Educational Expansion
Bibliographic record
Abstract
Family networks are key to understanding the wellbeing of older adults because kin provide instrumental and financial support, help manage health and disability, and encourage social integration. Two momentous societal changes have shaped the families of contemporary older adults: the first and second demographic transitions and global educational expansion. The intersection of these two processes raises questions about how older adults are faring in terms of their kin availability. This paper examines the socioeconomic bifurcation of adults in midlife and beyond in terms of the existence of descendants and other kin. Disparities in kin availability may vary across socioeconomic status and contexts, and so we examine this phenomenon worldwide, analyzing data on two thirds of the world's population of adults aged 50 and above. Our results highlight different kin structures by socioeconomic status. High socioeconomic status adults have fewer descendants but a higher likelihood of having at least one child with tertiary education, a partner, and living parents. Low socioeconomic status older adults have larger families with more younger kin. Our results shed new light on potential mismatches between the contemporary family networks of older adults and longstanding social norms and assumptions about caregiving, family, and health policies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".