Who am I and what do my peers think: How do gender identity and peer norms relate to other‐gender friendships
Bibliographic record
Abstract
Abstract Though there is increasing awareness of the potential benefits of other‐gender interactions and friendships, there has been little research examining the factors that might act as barriers or promoters of such friendships. Our goal was to explore both individual‐level factors (i.e., gender identity) and indicators of the social environment (i.e., perceived peer norms) that might relate to other‐gender friendships. Sixth graders (N = 396, 47% girls; 65% White) nominated friends in their classrooms, reported their similarity to both own‐ and other‐gender peers and reported on perceived peer norms related to other‐gender interaction. Results indicated that, in general, feeling similar to other‐gender youth was associated with more other‐gender friends. Positive peer norms (e.g., heightened respect) but not negative norms (e.g., teasing) also were associated with more other‐gender friends. Findings have implications for the importance of broadening our understanding of friendship homophily to consider gendered interests/similarities in addition to gender category. Further, the promotion of positive peer norms over negative ones might be an effective means of intervention to promote other‐gender friendships.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".