Sibling Availability, Sibling Sorting, and Subjective Health Among Chinese Adults
Bibliographic record
Abstract
Despite rising numbers of only children in China, little is known about their family dynamics and well-being in adulthood-for example, how often they marry other only children and whether those in siblingless families have worse or better health than others. Theoretical expectations produce opposing predictions: siblings might provide social and emotional support and reduce parental caregiving pressures, but only children might receive more support from parents and grandparents. Using the 2010 China Family Panel Study, we examine marital sorting on Chinese adults' number of siblings and test whether sibling availability and sibling sorting are associated with subjective physical and mental health. Despite general perceptions that China has an exceedingly high prevalence of adults with no siblings that might produce very small families, results demonstrate a low prevalence of siblingless couples (i.e., both spouses are only children). Married adults with no siblings or siblings-in-law have better subjective physical health but similar levels of subjective mental health relative to their counterparts with siblings. The health advantages of siblingless marital unions are greater for rural and female adults. Declining sibling prevalence in China will shape future family demographic dynamics but appears less detrimental to population health than sometimes assumed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".