The role of maternal factors in sibling relationship quality: a multilevel study of multiple dyads per family
Bibliographic record
Abstract
BACKGROUND: Although many children grow up with more than one sibling, we do not yet know if sibling dyads within families show similarities to one another on sibling affection and hostility. In the present study the hypotheses were tested that (a) there will be significant between family variation in change in sibling affection and hostility and (b) this between family variation will be explained by maternal affective climate, operationalized as positive and negative ambient parenting, differential parenting and maternal malaise. METHODS: A general population sample of families with single and multiple sibling dyads were visited twice, 2 years apart. Up to 2 children in a family acted as informants; 253 relationships were rated in 118 families. A cross-classified, multilevel model was fit to separate between-family and within-family variance in sibling relationships while simultaneously controlling for informant and partner influences. RESULTS: Thirty-seven percent of the variance in change in sibling affection and 32% of the variance in change in sibling hostility was between family variance. The measured maternal affective climate including, maternal malaise and maternal ambient and differential hostility and affection explained between family differences. CONCLUSIONS: Sibling relationship quality clusters in families and is partly explained by maternal affective climate.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".