Relocating Prejudice: A Transnational Approach to Understanding Immigrants’ Racial Attitudes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Immigration is changing the racial composition of many societies. Yet leading theories of racial prejudice, even in a multiracial context, focus on dynamics in a single nation-state and fail to account for the experiences of the foreign-born. We adopt a transnational approach that incorporates processes creating prejudice from both inside and outside the receiving society and that shows how attitudes move across borders through immigration, transnationalism, and globalization. We draw upon two in-depth studies of immigrants and those who stay in the home countries, focusing on Koreans’ and Dominicans’ attitudes toward Black Americans. By situating existing theories of racial prejudice within a transnational framework, we illustrate how models of transnationalism are relevant not just within immigration scholarship, but to more general processes of social change.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it