Culture influences the development of children's gender‐related peer preferences: Evidence from China and Thailand
Bibliographic record
Abstract
Current understanding of how culture relates to the development of children's gender-related peer preferences is limited. To investigate the role of societal acceptance of gender nonconformity, this study compared children from China and Thailand. Unlike China and other cultures where the conceptualization of gender as binary is broadly accepted, individuals who identify as a nonbinary "third" sex/gender have been highly visible and tolerated in Thai society for at least several decades. Chinese and Thai 4- to 9-year-olds (N = 458) viewed vignettes of four hypothetical peers who varied on gender (i.e., boy vs. girl) and gender-typed toy play behavior (i.e., masculine vs. feminine), and were asked to give a friendship preference rating for each peer. Chinese, compared with Thai, children evidenced gender-related peer preferences that emerged earlier, remained more stable across age groups, and were relatively more biased against gender-nonconforming behavior. The only cultural similarity was in children's preference for peers who were of the same gender and/or displayed same-gender-typed behavior. Thus, while preference for peers who are of the same gender and/or display same-gender-typed behavior is common among children across cultures, the developmental onset and course of these preferences vary by culture. Moreover, societal acceptance of gender nonconformity might be key to limiting children's bias against gender-nonconforming peers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".