Reciprocal and complementary sibling interactions, relationship quality and socio‐emotional problem solving
Bibliographic record
Abstract
Abstract Associations between reciprocal and complementary sibling interactions, sibling relationship quality, and children's socio‐emotional problem solving were examined in 40 grade 5–6 children (Mage = 11.5 years) from middle class, Caucasian, Canadian families using a multi‐method approach (i.e. interviews, self‐report questionnaires, daily diary checklist, narrative task). Findings demonstrated that reciprocal sibling interactions were positively associated with warmth, mutual esteem, happy daily exchanges, and negatively related to rivalry and dominance, whereas complementary interactions were positively related to upsetting daily exchanges. Further, reciprocal and complementary interactions differed significantly in relation to several relationship qualities, with reciprocal interactions emerging as a significantly stronger correlate of happy exchanges. Only reciprocal interactions were positively correlated with socio‐emotional problem solving. Finally, birth order moderated the negative association of reciprocal interactions with rivalry and dominance and the positive association with socio‐emotional problem solving. In each case, the effect was stronger for younger members of the sibling dyad. Findings are discussed in light of recent theory on the sibling relationship and children's development. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".