A Comparison of Divorce Risk Models in China and the United States
Bibliographic record
Abstract
Divorce has become more common in China. However, studies of Chinese marriage and family life have lagged because relevant data are sparse. Using a divorce risk model, this study compares China and the United States. Chinese data came from a 2007-2008 Shanghai study containing 1,070 married or cohabiting respondents. The U.S. data came from the 2000 Survey of Marriage and Family Life containing 2,100 married individuals. In both countries, marital quality (happiness) was higher when couples frequently shared activities, reported few arguments, had not experienced marital violence, thought it would be difficult to replace their current spouses, and grew up with happily married parents. Moreover, in both countries, divorce risk (thinking about divorce) was negatively associated with marital quality, marital duration, believing that marriage is for life, reporting few quarrels, and the absence of marital violence. Differences between samples also emerged, which may reflect unique cultural aspects of Chinese and American society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".