A Cross-Cultural Investigation of Minority and Non-White Majority Children's Implicit Attitudes Toward Racial Outgroups
Bibliographic record
Abstract
In this research I aimed to increase our understanding of the early emergence of racial biases by examining the implicit racial attitudes of minority and non-White majority children in two cultures. In Study 1, minority children in Canada completed an Implicit Association Test to measure implicit racial attitudes. Young non-Black minority children held a pro-White (versus Black) implicit bias. However, unlike previous findings, the magnitude of bias was lower for older children. In Study 2, I examined the implicit attitudes of Malay (majority) and Chinese (minority) children and adults in Brunei with limited contact with White or Black peers. Children showed implicit pro-White and pro-Chinese (versus Black) biases by early childhood, but showed no pro-White (versus Chinese) bias. Together, these findings support theorizing about the development of implicit intergroup cognition (Dunham et al., 2008), but suggest that context can shape these biases to a greater extent than was previously thought.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".