Contextualizing Anti-Immigrant Attitudes of East Europeans
Bibliographic record
Abstract
This paper article examines attitudes toward immigrants by analyzing data from the 2010 and 2016 waves of the EBRD’s Life in Transition Survey among respondents from 16 East European countries. Logistic regressions with clustered standard errors and country fixed effects show significantly higher anti-immigrant sentiments after the 2015 immigration pressures on the European Union borders compared with attitudes in 2010. Almost two thirds of the respondents agreed in 2016 that immigrants represented a burden on the state social services, even when the actual immigrant population in these countries was quite small. In addition, East Europeans expressed greater negative sentiments when the issue of immigration was framed as an economic problem—a burden on state social services—than as a cultural problem—having immigrants as neighbors. On the whole, these results point to the importance of contextualizing anti-immigrant attitudes and understanding the effect of external events and the framing of immigration-related survey questions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".