The Ukrainian Refugee Crisis and the Politics of Public Opinion: Evidence from Hungary
Bibliographic record
Abstract
The 2022 Russian invasion of Ukraine was a watershed moment in European politics. The invasion prompted a massive influx of refugees into Central Europe, a region in which immigration has proven highly contentious and politically salient in recent decades. We study public opinion toward refugees in Hungary, a highly exclusionary political environment in which anti-migrant and anti-refugee sentiments are commonly invoked by the ruling government. Combining historical public opinion data from the past decade with two rounds of original survey data from 2022, we demonstrate that the Ukrainian refugee crisis was accompanied by a large increase in tolerance for refugees, reversing what had previously been one of the most anti-refugee public opinion environments in Europe. To explain this reversal, we use a series of survey experiments coupled with detailed settlement-level demographic data to investigate how conflict proximity and racial, religious, and national identity shape openness to refugees. We find that the distinguishing feature of the 2022 refugee crisis was that refugees were mostly white European Christians driven from their home country by conflict. We discuss the implications of our argument for Hungary, for European politics in times of crisis, and for the politics of public opinion in competitive authoritarian regimes.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".