Testing an intergroup relations intervention strategy to improve children’s appraisals of gender-nonconforming peers
Bibliographic record
Abstract
Past research has linked poor psychosocial adjustment among children who express gender nonconformity (GNC) to poor peer relations (e.g., facing ridicule and rejection); however, very little research has explored whether it is possible to improve children’s appraisals of GNC. The present study attempted to replicate a previous intervention that was conducted among 8- to 9-year-old children from Hong Kong that successfully improved children’s appraisals of gender-nonconforming peers. Specifically, it tested whether the same intervention was successful at improving appraisals of gender-nonconforming peers in a sample of children from Canada and among both 4- to 5-year-old and 8- to 9-year-old children. To do so, we employed an experimental vignette design among 4- to 5-year-old ( n = 176; 48% girls) and 8- to 9-year-old ( n = 182; 49% girls) children. In the intervention condition, targets were presented who displayed mostly gender-nonconforming preferences, some gender-conforming preferences, and positive attributes. Following the intervention, participants’ appraisals of gender-nonconforming and gender-conforming targets were assessed through verbal reports, a sharing task, and a rank-order task. Overall, the intervention did not improve appraisals of GNC, and there were no differences based on age or gender of the participants, or gender of the targets. We discuss possible reasons why there was a cultural difference in the effectiveness of the intervention and how future intervention work in this area might be strengthened.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".