Americans preferred Syrian refugees who are female, English-speaking, and Christian on the eve of Donald Trump’s election
Bibliographic record
Abstract
What types of refugees do Americans prefer for admission into the United States? Scholars have explored the immigrant characteristics that appeal to Americans and the characteristics that Europeans prioritize in asylum-seekers, but we currently do not know which refugee characteristics Americans prefer. We conduct a conjoint experiment on a representative sample of 1800 US adults, manipulating refugee attributes in pairs of Syrian refugee profiles, and ask respondents to rate each refugee's appeal. Our focus on Syrian refugees in a 2016 survey experiment allows us to speak to the concurrent refugee crisis on the eve of a polarizing election, while also identifying religious discrimination, holding constant the refugee's national origin. We find that Americans prefer Syrian refugees who are female, high-skilled, English-speaking, and Christian, suggesting they prioritize refugee integration into the U.S. labor and cultural markets. We find that the preference for female refugees is not driven by the desire to exclude Muslim male refugees, casting doubt that American preferences at the time were motivated by security concerns. Finally, we find that anti-Muslim bias in refugee preferences varies in magnitude across key subgroups, though it prevails across all sample demographics.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 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".