“He did girls’ things!” Hong Kong and Canadian children’s reasoning about moral judgments of peers’ gendered behaviors.
Bibliographic record
Abstract
= 678) reason about their moral judgments of GC and GN peers. After viewing vignettes describing GC and GN boys and girls, we asked children whether each target peer's behavior was right or wrong and why they thought so. We coded children's reasoning using a new coding scheme developed via inductive content analysis. Overall, children's most commonly used reasoning styles were global standard, personal choice, gender stereotypes, "don't know," and others' welfare. Children used more gender stereotype-related reasoning when they were older and from Hong Kong, appraising the GN boy, or when they perceived the target's behavior as wrong. In contrast, children reasoned based on personal choice more when they were from Canada or when they perceived the target's behavior as right. These findings inform how age-, gender-, and culture-related factors are associated with children's reasoning about the acceptability or appropriateness of varying kinds of childhood gendered behavior. They provide insights regarding children's appraisals of different gender expressions by illuminating not only how they view GC and GN peers but also, from their own perspectives, why they do so. These insights have implications for strategies aimed at decreasing gender-related biases and increasing children's acceptance of gender diversity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".