Inter-minority Relations: Factors Shaping Cognitive and Affective Intergroup Attitudes between Asian and Black Americans
Bibliographic record
Abstract
Abstract Rising anti-Asian racism and the recent police killings of unarmed Black people have called attention to how Asian and Black Americans experience racism and how they perceive one another. Using data from a recent national sample of Asian (n = 1078) and Black Americans (n = 367), we explored socio-demographic (demographic, socioeconomic, political, and immigration) as well as group-relevant predictors of intergroup attitudes between Asian and Black Americans. Measures of intergroup attitudes included feelings of warmth and negative outgroup sentiment. Regression analyses showed that income, educational attainment level, employment status, immigration status, gender, age, ethnicity, political ideology, and political party affiliation were significant socio-demographic predictors of Asian Americans’ attitudes toward Black Americans. In contrast, only age and ethnicity emerged as significant socio-demographic predictors of Black Americans’ attitudes toward Asian Americans. The explanatory power of beliefs about group relations–such as endorsement of zero-sum, nationalist, and oppressed minority ideologies–as well as the degree of intergroup contact was quite strong for predicting intergroup attitudes for both groups. The findings reveal the complexity behind Asian-Black intergroup dynamics and highlight pathways and barriers toward cultivating more positive attitudes and intergroup relations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".