Cross-Setting Correspondence in Sociometric Nominations Among Children With Attention-Deficit/Hyperactivity Disorder
Bibliographic record
Abstract
Peer problems are common among children with emotional and behavioral disorders (EBD). However, the extent to which children’s peer functioning varies across settings is unknown, as is the incremental power of peer functioning in different settings in predicting subsequent psychopathology. Participants were 57 children with Attention-Deficit/Hyperactivity Disorder (ADHD) who had peer sociometric nominations collected in general education classrooms and a Summer Treatment Program (STP) with all-EBD peers. Significant, small-to-medium correlations existed between nomination rates across settings. Lower rates of STP positive nominations and higher rates of STP negative nominations (but not classroom nominations) predicted exacerbated self-reported depression and antisocial behavior 1 year later. Lower rates of STP positive nominations, but higher rates of classroom positive nominations, predicted increased self-reported depression 2 years later. For children with high rates of classroom positive nominations, higher rates of STP positive nominations predicted reduced parent-reported internalizing behavior 1 year later. For children with low rates of classroom negative nominations, higher rates of STP negative nominations predicted increased teacher-reported externalizing behavior 1 year later and self-reported depression 2 years later. Results suggest that sociometric nominations collected within an EBD peer group may have stronger predictive power for later adjustment than nominations collected in a mainstream classroom.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".