Sources of implicit and explicit intergroup race bias among African-American children and young adults
Bibliographic record
Abstract
Implicit intergroup bias emerges early in development, are typically pro-ingroup, and remain stable across the lifespan. Such findings have been interpreted in terms of an automatic ingroup bias similar to what is observed with minimal groups paradigms. These studies are typically conducted with groups of high cultural standing (e.g., Caucasians in North America and Europe). Research conducted among culturally lower status groups (e.g., African-Americans, Latino-Americans) reveals a notable absence of an implicit ingroup bias. Understanding the environmental factors that contribute to the absence of an implicit ingroup bias among people from culturally lower status groups is critical for advancing theories of implicit intergroup cognition. The present study aimed to elucidate the factors that shape racial group bias among African-American children and young adults by examining their relationship with age, school composition (predominantly Black schools or racially mixed schools), parental racial attitudes and socialization messages among African-American children (N = 86) and young adults (N = 130). Age, school-type and parents' racial socialization messages were all found to be related to the strength of pro-Black (ingroup) bias. We also found that relationships between implicit and explicit bias and frequency of parents' racial socialization messages depended on the type of school participants attended. Our results highlight the importance of considering environmental factors in shaping the magnitude and direction of implicit and explicit race bias among African-Americans rather than treating them as a monolithic group.
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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 | 0.003 |
| 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 | 0.000 |
| 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".