Examining Children's Implicit Racial Attitudes Using Exemplar and Category-Based Measures
Bibliographic record
Abstract
The goal of this research was to examine children's implicit racial attitudes. Across three studies, a total of 359 White 5- to 12-year-olds completed child-friendly exemplar (Affective Priming Task; Affect Misattribution Procedure) and category-based (Implicit Association Test) implicit measures of racial attitudes. Younger children (5- to 8-year-olds) showed automatic ingroup positivity toward White child exemplars, whereas older children (9- to 12-year-olds) did not. Children also showed no evidence of automatic negativity toward Black exemplars, despite demonstrating consistent pro-White versus Black bias on the category-based measure. Together, the results suggest that (a) implicit ingroup and outgroup attitudes can follow distinct developmental trajectories, and (b) the spontaneous activation of implicit intergroup attitudes can depend on the salience of race.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 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) | 0.000 | 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 teacher head, 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".