Balanced, positive, and negative attributions: A preliminary investigation of a novel attribution coding system and associated affect and social behavior in children with disruptive behavior
Bibliographic record
Abstract
Abstract Research on children's social information processing (SIP) has mainly focused on negative attributions in peer provocation and rejection situations. The potential of balanced attributions—attributing both positive and negative intent—and of positive attributions has not been explored. We conducted a series of regressions to examine balanced, positive, and negative attributions and links to affective response and socioemotional functioning in 8 to 12 year old (M = 10.30; SD = 1.09; N = 111) that were clinic‐referred for disruptive behavior. Children's responses to hypothetical situations resulting in ambiguous‐positive and ambiguous‐negative situations were coded for positive, negative, or balanced attribution or affect. Caregivers reported on children's social and emotional functioning. Results indicated that a proportion of children (21.6%) made at least one balanced attribution in both types of situations. Affective responses tended to be in line with attribution style, with positive attribution linked to positive affect, balanced attribution linked to mixed affect, and negative attribution linked to negative affect. Children making positive attributions in ambiguous‐positive situations and balanced attributions across situations tended to have less negative functioning and more positive functioning. Reconsideration of attribution coding schemes to include balanced and positive attributions may guide theoretically important and novel directions in SIP research.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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".