Etiology and measurement of relational aggression: A multi-informant behavior genetic investigation.
Bibliographic record
Abstract
Although the study of relational aggression is gaining attention in the literature, little is known about the underlying causes of this behavior and the relative validity of various informants. These issues were addressed in a sample of 1,981 6- to 18-year-old twin pairs (36% female, 34% male, 30% opposite-sex). Relational aggression was assessed via maternal and self-report using a structured interview. Univariate models estimated genetic and environmental influences by informant and examined evidence for gender differences. A psychometric model combined data from both informants to estimate etiologic influences that were both common to the informants and informant specific. In both sexes, the latent variable reflecting the mother's and child's shared perception of the child's relational aggression was substantially influenced by both additive genetic (63%) and shared environmental (37%) influences, although this latent variable accounted for much greater variance in the maternal report (66%) than it did in the youth report (9%). In addition, informant-specific additive genetic and shared environmental influences were found only for the youth report, with all remaining variance in the mother's report attributed to nonshared environmental influences. Results are discussed in the context of measuring relational aggression and the importance of multiple informants.
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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 | 0.006 | 0.013 |
| 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 | 0.000 |
| Open science | 0.001 | 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".