Kinlessness Around the World
Bibliographic record
Abstract
OBJECTIVES: The first and second demographic transitions have led to profound changes in family networks. However, the timing and extent of these transitions vary widely across contexts. We examine how common it is for contemporary older adults to lack living kin and whether such individuals are uniformly disadvantaged around the world. METHODS: Using surveys from 34 countries that together contain 69.6% of the world's population over age 50 and come from all regions of the world, we describe the prevalence and correlates of lacking immediate kin. We examine macro-level demographic indicators associated with the prevalence of kinlessness as well as micro-level associations between kinlessness and sociodemographic and health indicators. RESULTS: There is great variation in levels of kinlessness, from over 10% with neither a spouse nor a biological child in Canada, Ireland, the Netherlands, and Switzerland to levels below 2% in China and the Republic of Korea. There are strong macro-level relationships between kinlessness and lagged or contemporaneous fertility, mortality, and nuptiality measures and more marginal relationships with other demographic forces. Micro-level associations between kinlessness and respondent attributes are varied. The kinless are more likely to live alone than those with kin in all countries. In most countries, they have equivalent or worse self-rated health and lower education, although there are notable exceptions. There is substantial variation in the gender composition of the kinless population. DISCUSSION: As demographic changes affecting kinlessness continue, we expect the scale of the kinless population to grow around the world.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".