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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".