American Public Opinion on Immigration: Nativist, Polarized, or Ambivalent?
Bibliographic record
Abstract
Abstract For Dauvergne (2016), one consequence of the “end of settler societies” is nativism, or what she calls “mean‐spirited politics”: anti‐immigrant, anti‐Muslim, anti‐Multiculturalism. This accords with the prevailing tone of public opinion literature on the subject, which links anti‐immigrant hostility in settler societies to influxes of diversity and associated racial threat. In this essay, we determine just how closely this stylized vision of anxiety‐fuelled nativism resembles the true state of mass opinion about immigration. Using a variety of surveys fielded in recent years, we show that Americans: 1) hold generally positive views about immigration, though with a substantial dose of ambivalence about its consequences; 2) are not especially consistent in their policy attitudes over time; 3) express policy attitudes that readily depart from their underlying predispositions, and; 4) have only become more pro‐immigrant in recent years, and whatever partisan polarization exists on the issue stems from the fact that Republicans are becoming more positive at a slightly slower pace than Democrats. All of this suggests that, while there is a hard core of ethnocentrism and "mean‐spiritedness" in the U.S., the prevailing tone is much less negative than the standard portrayal assumes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".