Family members' strategies as allies during naturalistic polyadic family conflicts
Bibliographic record
Abstract
Abstract This study investigated the strategies used by family members as allies during naturalistic polyadic family conflicts (i.e., involving at least three family members). Thirty‐nine families with two children (aged 4 and 6 years) were observed in the home setting. Employing transcripts of their interactions, emergent coding revealed five strategies used by allies: (a) social rule understanding, (b) control, (c) informational, (d) simple (e.g., dismissive), and (e) elaborate (e.g., proposals). Overall, both parents and children used control and informational strategies most often. Parents used each of the five strategies more often than both children, but control and informational strategies were predominant. Similarly, when older siblings assumed the ally role, they preferred control strategies, whereas younger siblings used informational strategies most frequently. The results emphasize the role of family members as allies during conflicts at home and are discussed in relation to parents' and children's cognitive awareness and learning of such strategies. Highlights This paper examines the strategies employed by family members as allies during naturalistic polyadic conflicts (i.e., involving three or more family members). Families were observed at home. Parents and children preferred control and informational strategies, suggesting a social learning component during conflicts. Children's involvement as allies highlights their awareness of alliances and the diverse array of strategies associated with this role.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".