Sex differences in aggression among children of low and high gender inequality backgrounds: A comparison of gender role and sexual selection theories
Bibliographic record
Abstract
It is well understood in aggression research that males tend to exhibit higher levels of physical aggression than females. Yet there are still a number of gaps in our understanding of variation in sex differences in children's aggression, particularly in contexts outside North America. A key assumption of social role theory is that sex differences vary according to gender polarization, whereas sexual selection theory accords variation to the ecological environment that consequently affects male competition [Archer, J. (2009). Behavioral and Brain Sciences, 32, 249-311; Kenrick, D., & Griskevicious, V. (2009). More holes in social roles [Comment]. Behavioral and Brain Sciences, 32, 283-285]. In the present paper, we explore these contradicting theoretical frameworks by examining data from a longitudinal study of a culturally diverse sample of 863 children at ages 7-13 in Zurich, Switzerland. Making use of the large proportion of children from highly diverse immigrant background we compare the size of the sex difference in aggression between children whose parents were born in countries with low and with high levels of gender inequality. The results show that sex differences in aggression are generally larger among children with parents from high gender inequality backgrounds. However, this effect is small in comparison to the direct effect of a child's biological sex. We discuss implications for future research on sex differences in children's aggression.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".