Children’s Appraisals of Gender Nonconformity: Developmental Pattern and Intervention
Bibliographic record
Abstract
Gender-nonconforming (GN) children are often perceived less positively, which may harm their well-being. We examined the development of such perceptions and an intervention to modify them. Chinese children's appraisals were assessed using multiple measures (verbal responses, sharing, and rank order task) after viewing vignettes of gender-conforming (GC) and GN hypothetical peers. In Study 1, children (N = 210; 4-, 5-, 8-, and 9-year-olds) were less positive toward GN than GC peers, especially if they were older or if the peers were boys. In Study 2 (N = 211, 8- and 9-year-olds), showing children exemplars of GN peers who displayed positive and GC characteristics subsequently reduced bias against gender nonconformity. These findings inform strategies aimed at reducing bias against gender nonconformity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".