Differentiating typical from atypical perpetration of sibling‐directed aggression during the preschool years
Bibliographic record
Abstract
BACKGROUND: Sibling aggression is common and often viewed as benign. Although sibling aggression can be harmful for the victims, it may also be a marker of clinical risk for the aggressor. We differentiated typical from atypical levels of perpetration of sibling-directed aggression among preschoolers, a developmental period in which aggression is a normative misbehavior, by (a) identifying how frequently aggressive behaviors targeted at a sibling must occur to be psychometrically atypical; (b) mapping the dimensional spectrum of sibling-directed aggression from typical, more commonly occurring behaviors to rarer, more atypical, actions; and (c) comparing the psychometric atypicality and typical-to-atypical spectrum of sibling-directed aggression and peer-directed aggression. METHODS: Parents (N = 1,524) of 3- (39.2%), 4-(36.7%), and 5-(24.1%) year-olds (51.9% girls, 41.1% African-American, 31.9% Hispanic; 44.0% below the federal poverty line) completed the MAP-DB, which assesses how often children engage in aggressive behaviors. We used item-response theory (IRT) to address our objectives. RESULTS: Most aggressive behaviors toward siblings were psychometrically atypical when they occurred 'most days' or more; in contrast, most behaviors targeted at peers were atypical when they occurred 'some days' or more. With siblings, relational aggression was more atypical than verbal aggression, whereas with peers, both relational and physical aggression were more atypical than verbal aggression. In both relationships, the most typical behavior was a verbally aggressive action. Results were broadly replicated in a second, independent sample. CONCLUSIONS: These findings are a first step toward specifying features of sibling aggression that are markers of clinical risk and belie the notion that sibling aggression is inherently normative.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".