Maternal Warmth and Prosocial Behaviors Among Chinese Preschool Children: The Roles of Social Competence Sibling Presence
Bibliographic record
Abstract
Research Findings: Despite the fact that the universal two-child policy had a significant impact on family structure and parenting behaviors, there is still a paucity of study on the mechanisms underlying this policy’s effects. This study explored the role of social competence and sibling presence in the relationship between maternal warmth and prosocial behaviors. Participants were 203 children aged 5–6 years (Mage = 48.47 months, SD = 3.74;104 boys) and their mothers in China. Maternal warmth was assessed through the observation of mother – child interaction. Child social competence and prosocial behaviors were assessed by teacher. The results showed that:(1) only children had more prosocial behaviors than children with siblings; (2) maternal warmth (T1) was positively correlated with child prosocial behaviors at Time1 and Time2; (3) Social competence (T2) mediated the longitudinal association between maternal warmth (T1) and child prosocial behaviors (T2); (4) The sibling presence moderated the relationship between maternal warmth (T1) and child prosocial behaviors (T2). Specifically, maternal warmth was only associated with subsequent prosocial behaviors for children in one-child families. Practice or Policy: These results revealed the influence of family structure and maternal warmth on child prosocial behaviors especially for children with siblings.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".