Ethnocentrism versus group-specific stereotyping in immigration opinion: cross-national evidence on the distinctiveness of immigrant groups
Bibliographic record
Abstract
While widespread resistance to immigration is well established in advanced democracies around the world, the role of group-specific stereotyping in anti-immigration sentiment has received limited attention. We derive a novel measurement model to assess stereotyping in three Anglo-Saxon democracies – the US, Canada, and the UK – of the modal outgroup in each country (Hispanics in the US and South Asians in Canada and the UK) and Middle Easterners/Muslims. We show that considerable variation exists in degree of stereotyping against the two major immigrant groups. In the US case, we additionally document over-time variation in group stereotyping. In a final step, we demonstrate a relationship between group antipathies and immigration policy views, akin to other policy domains in which public support varies by the ethnic characteristics of policy beneficiaries. To our knowledge, this study is the first to map stereotypes of Muslims in the US in a comparative setting and over time after 09/11, and amongst the first to link views on immigration policies to group-based stereotypes.
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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 | 0.003 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".