Civilizing vs destructive globalization? A multi-level analysis of anti-immigrant prejudice
Bibliographic record
Abstract
This study investigates the impact of the latest wave of globalization on anti-immigrant prejudice. We discern and test two contradictory accounts of the impact of globalization on anti-immigrant prejudice from the prejudice and globalization literatures. On the one hand, there is the ‘civilizing/integrative globalization’ thesis, which implies that globalization should help to decrease prejudice by creating sustained and equal contact between previously alien cultures and peoples, and by spreading economic gains to everybody. On the other hand, there is the ‘destructive globalization/globalization as a threat’ thesis, which argues that globalization should increase anti-immigrant prejudice by intensifying competition over resources and by increasing perceived threat by native populations as a result of increasing immigrant populations. We test these two accounts using a multi-level analysis of 64 countries and nearly 150,000 individuals, derived from the World Values Surveys (waves 3–5). Our analyses reveal support for ‘destructive globalization/ globalization as a threat’ thesis, but emphasize the multi-dimensional character of globalization. We find that citizens of countries with higher levels of trade openness have significantly more anti-immigrant sentiments. There is also some evidence that in countries where unemployment is accompanied by high levels of trade openness or the existence of large immigrant populations, citizens hold high anti-immigrant prejudice. By contrast, foreign direct investment (FDI) has a weak effect.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".