Children’s implicit attitudes toward targets who differ by race and gender.
Bibliographic record
Abstract
= 206; 109 boys, 97 girls; 55% White; 68% of household incomes > $75,000/year), recruited from a science museum in a large multicultural Canadian city, completed a child-friendly Implicit Association Test (IAT; Greenwald et al., 2003) that included own-gender Black and other-gender White targets. Children were randomly assigned to complete this IAT under one of three categorization conditions. When asked to categorize targets by gender as opposed to race, both girls and boys showed relatively more positive associations with own-gender Black targets over other-gender White targets. Children in a third, Ambiguous-Categorization (AC-IAT; Lipman et al., 2021) condition, which allowed for categorization by gender and/or race, were more likely to spontaneously categorize additional final trials primarily by gender (70%), suggesting that gender was the more salient social category. However, girls' and boys' biases in this condition differed, with girls showing relatively more positive associations with own-gender Black targets (Black girls > White boys) and boys showing relatively more positive associations with other-gender White targets (White girls > Black boys). In addition, the more boys and girls categorized by gender (over race) at the end of the task, the more they showed positive associations with own-gender Black targets over other-gender White targets. These findings provide insight into children's social categorization processes and biases toward targets who differ by race and gender. (PsycInfo Database Record (c) 2023 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".