“ <i>No! The Lambs Can Stay Out Because They Got Cozies</i> ”: Constructive and Destructive Sibling Conflict, Pretend Play, and Social Understanding
Bibliographic record
Abstract
Associations among constructive and destructive sibling conflict, pretend play, internal state language, and sibling relationship quality were investigated in 40 middle-class dyads with a kindergarten-age child (M age = 5.7 years). In 20 dyads the sibling was older (M age = 7.1 years) and in 20 dyads the sibling was younger (M age = 3.6 years). Dyads were videotaped playing with a farm set for 15 min; transcribed sessions were coded for (1) five types of conflict issues; (2) constructive, destructive, and passive resolution strategies; and (3) verbal and physical aggression. Measures of pretend play enactment, low- and high-level pretense negotiation strategies, and internal state language were also based on the transcripts. The Sibling Behavior and Feelings Questionnaire was used to assess both siblings' perceptions of sibling relationship quality. Findings revealed that conflict issues, aggression, and internal state language were associated with specific resolution strategies. Associations were evident between conflict issues and resolutions. Moreover, conflict issues and resolutions were associated with (1) relationship quality, (2) high-level pretense negotiation, and (3) internal state language employed in both play and conflict. Findings are discussed in light of recent theory on developmental processes operating within children's relationships.
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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 | 0.000 |
| 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".