Conversations about children's transgressions against siblings and friends: Maternal moral socialization strategies are sensitive to relationship context
Bibliographic record
Abstract
Abstract This study investigated the moral socialization strategies that mothers use in conversations about their children's experiences of harming their siblings as compared with their friends. The sample included 101 mothers and their 7‐, 11‐, or 16‐year‐old children; each dyad discussed events when the child (a) harmed a younger sibling and (b) harmed a friend (order counterbalanced). Analyses indicated that when children harmed their siblings, mothers were more likely to emphasize their children's wrongdoings, the effects of harm, insights about the self and de‐escalatory strategies. In contrast, when children harmed their friends, mothers mitigated their responsibility for harm, focused on consequences for the relationship, and discussed reparative strategies. These patterns were not typically moderated by age; that is, distinctions in socialization strategies across relationships were largely maintained across middle childhood and adolescence. Findings provide empirical support for recent theorizing that parents respond flexibly to children's varied experiences of harm, and suggest that moral socialization practices take into account the unique features of children's relationships with different types of age‐mates (e.g., the relatively uninhibited quality of sibling harm and the voluntary nature of children's friendships).
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".