The “Refugee Crisis,” Immigration Attitudes, and Euroscepticism
Bibliographic record
Abstract
Between 2015 and 2017, the European Union (EU) was confronted with a major crisis in its history, the so-called “European refugee crisis.” Since the multifaceted crisis has provoked many different responses, it is also likely to have influenced individuals’ assessments of immigrants and European integration. Using data from three waves of the European Social Survey (ESS) — the wave before the crisis in 2012, the wave at the beginning of the crisis in 2014, and the wave right after the (perceived) height of the crisis in 2016 — we test the degree to which the European refugee crisis increased Europeans’ anti-immigrant sentiment and Euroscepticism, as well as the influence of Europeans’ anti-immigrant attitudes on their level of Euroscepticism. As suggested by prior research, our results indicate that there is indeed a consistent and solid relationship between more critical attitudes toward immigrants and increased Euroscepticism. Surprisingly, however, we find that the crisis increased neither anti-immigrant sentiments nor critical attitudes toward the EU and did not reinforce the link between rejection of immigrants and rejection of the EU. These findings imply that even under a strong external shock, fundamental political attitudes remain constant.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".