How Children's Justifications of the "Best Thing to Do" in Peer Conflicts Relate to Their Emotional and Behavioral Problems in Early Elementary School
Bibliographic record
Abstract
In this three-year longitudinal study, children were asked to choose the "best" strategy for dealing with hypothetical peer provocations and to justify "why" that was their choice at the end of first, second, and third grades. Teachers and parents also rated children's emotional and behavioral problems. Children's justifications were subjected to qualitative analyses to identify distinct content categories. These included getting others into trouble or avoiding it, dichotomous reasoning about good (kind) versus bad (mean) strategies, appeals to authorities for help, situation-specific solutions that anticipated consequences of actions, or general rules or solutions that could or should be used in similar conflicts to effect positive outcomes. These justification categories were related to the children's grade levels. Older children were more likely to use more story-specific justifications and to refer to the perspectives of others and to future consequences in their justification responses. Children who used justifications that involved getting others into trouble or avoiding it had higher levels of teacher ratings of concurrent emotional and behavioral problems at second and third grades and to parent ratings of emotional problems at third grade.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".