A cross‐cultural investigation of children’s implicit attitudes toward White and Black racial outgroups
Bibliographic record
Abstract
Initial theory and research examining children's implicit racial attitudes suggest that an implicit preference favoring socially advantaged groups emerges early in childhood and remains stable across development (Dunham, Baron, & Banaji, 2008). In two studies, we examined the ubiquity of this theory by measuring non-Black minority and non-White majority children's implicit racial attitudes toward White and Black racial outgroups in two distinct cultural contexts. In Study 1, non-Black minority children in an urban North American community with a large Black population showed an implicit pro-White (versus Black) bias in early childhood. Contrary to previous findings, the magnitude of this bias was lower among older children. In Study 2, Malay (majority) and Chinese (minority) children and adults in the Southeast Asian country of Brunei, with limited contact with White or Black peers, showed an implicit pro-White (versus Black) bias in early childhood. However, the magnitude of bias was greater for adults. Together, these findings support initial theorizing about the early development of implicit intergroup cognition, but suggest that context may affect these biases across development to a greater extent than was previously thought. A video abstract of this article can be viewed at: https://www.youtube.com/watch?v=vgQP8e4MSCk&feature=youtu.be.
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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 | 0.003 |
| 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 | 0.001 |
| 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".