Group Norms Moderate the Association between Individual-Level Characteristics and Peer Perceived Gender Typicality
Bibliographic record
Abstract
A multilevel analysis conducted with a cross-sectional sample of 324 pre -adolescents (N= 324) in grades five and six (Mage = 11.5) of girls (N= 170) and boys from three mixed-gender primary school schools in Montreal, Quebec, Canada and two mixed-gender schools in Barranquilla, Colombia (N= 174) was used to examine the effect of group norms on peer perceived gender typicality. Group level variables including descriptive same-gender and other-gender group norms for each of the social behaviors. SES, culture, and gender were examined as moderators of the association between four forms of gendered social behaviors, specifically care, justice, physical aggression, and relational aggression, and children’s perceptions of their peers as being typical members of their cis gender group categories. Multilevel modeling indicated distinct patterns of effects for the level 2 group variables on the intercept and slope for each of the four social behaviors. For the intercept, same-gender and other-gender group means were positively associated with gender typicality for three (care, justice and relational aggression) of the four behaviors. For the slope – gender typicality was high when other-gender group means for physical aggression, relational aggression and care were low. Only relational aggression was found to be a significant predictor at level 1. SES and place effects also found. The findings show that perceptions of gender typicality are contextually determined.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".