Bibliographic record
Abstract
Immigration is a major driver of ethnic and racial diversity. Attitudes towards immigration are heavily influenced by attitudes towards such diversity. Those with prejudicial attitudes are likely to prefer immigration policies that favour immigrants like themselves, or to prefer less immigration overall. In this chapter, we focus on the link between immigration attitudes and prejudice. After reviewing the dominant theoretical frameworks for understanding immigration attitudes, we provide an empirical illustration of how immigration is racialized in the minds of Canadians and Americans. Drawing on survey data from Canada and the United States, we explore how immigration is a racialized policy domain. First, we show how people’s feelings about immigrants as a group are strongly predicted by how people feel about ethnic and racial minorities. We argue that this ethnocentrism is in part tied to psychological predispositions like social dominance orientation and authoritarianism. Second, we show that ethnocentric attitudes also predict decreased support for immigration as a policy domain. We conclude by arguing that promoting more inclusive and open attitudes towards newcomers in a society requires that we target the types of intolerance that ethnic and racial minorities face within these societies, whether they are foreign-born or not.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".