Children’s spontaneous associations with targets who differ by race and emotional expression.
Bibliographic record
Abstract
Across three studies, we examined non-Black children's spontaneous associations with targets who differed by both race and emotional expression. Children aged 5 to 10 years (N = 419; 215 girls; 58% White; 65% of household incomes >$75,000/year) completed Implicit Association Tests (IAT; Greenwald et al., 2003) containing smiling Black and neutral White target faces. In all three studies, when children categorized these faces by emotional expression, they showed relatively more positive associations with smiling Black targets over neutral White targets, as compared with when they categorized these faces by race. This was the case when children were shown how to categorize these faces (Studies 1 and 2) and when they spontaneously categorized by race or emotional expression on an Ambiguous-Categorization IAT that allowed for categorization by race and/or emotion (Studies 2 and 3). In Study 3, after watching an adult explain that she was categorizing racially diverse faces by emotional expression in a seemingly unrelated card-sorting task, children were also relatively faster to pair smiling Black faces with pleasant images and neutral White faces with unpleasant images on this Ambiguous-Categorization IAT compared with children in a control condition. Older children were more likely to spontaneously categorize primarily by race (Studies 2 and 3) but were also more likely to categorize by emotion following the intervention (Study 3) compared with younger children. Together, these studies provide insight into children's social categorization processes and spontaneous associations with targets who differ systematically across multiple perceptually salient categories. (PsycInfo Database Record (c) 2021 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".