Parental Divorce, Family Structure, and Children’s Academic Performance in China
Bibliographic record
Abstract
This empirical study zeroes in on the effect of family structure on Chinese adolescents’ outcomes in the divorce context, an understudied area of research. Data from the second wave of the China Education Panel Survey (CEPS) undergoes multiple regression analysis and Structural Equation Modelling (SEM) to estimate the association between divorced single-parent family structure and children’s academic performance and examine its explanatory mechanisms. Results initially reveal a negative relationship between single-father families and children’s academic scores. Single-mother families also face disadvantages after controlling child characteristics. Furthermore, the impact of family structure is moderated by grandparental co-residence, demonstrating that children from single-father families benefit more from living with grandparents. Results also suggest the chain mediation of grandparental co-residence and children’s peer quality on the association between single-father families and child academic scores. The study highlights the complex role of grandparental co-residence and its influential effects in the interconnection between family and peer groups, advancing the theory of embedding mediation mechanisms into Bronfenbrenner’s framework and emphasizing the interactions unfold in the microsystem. Overall, this study encourages parents to take active roles in leveraging grandparental co-residence to enhance peer environments in their children’s development, especially in the case of divorced single-father families. It also points out potential variations in single parenthood by the gender of custodial parent for future study.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".