Is Relational Aggression Part of the Externalizing Spectrum? A Bifactor Model of Youth Antisocial Behavior
Bibliographic record
Abstract
The primary purpose of the present study was to examine support for the inclusion of relational aggression (RAgg) alongside physical aggression (Agg) and rule-breaking behaviors (RB) as a subfactor of antisocial behavior (ASB). Caregiver reports were collected for 1,087 youth (48.9% male) ages 6-18. Results indicated that all three subfactors of ASB demonstrated substantial loadings on a general ASB factor. Using a bifactor model approach, specific factors representing each ASB subfactor were simultaneously modeled, allowing for examination of common and specific correlates. At the scale level, results demonstrated consistently strong connections with high Neuroticism and low Agreeableness across all 3 ASB subfactors, a pattern which was replicated for the general ASB factor in the bifactor approach. Specific factors in the bifactor model demonstrated connections with personality and psychopathology correlates, primarily for Agg. These findings provide some support for an overall grouping of RAgg with other ASB subfactors in youth, and further distinguish Agg as potentially representing a more potent variant of youth ASB relative to both RB and RAgg.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".