Bibliographic record
Abstract
Abstract This article adds to the large literature on right-wing populist parties ( RWPP ), explaining how anti-immigrant sentiments become salient for vote choice. Within the large literature on RWPP , anti-immigration attitudes are the most important variable to explain the vote share of RWPP . Yet, recent research shows that there is not always an empirical effect between having anti-immigrant attitudes and voting for the RWPP . In this article, we develop a theoretical model that explains the conditions under which anti-immigration attitudes matter. We then test this model based on the case of the AfD in Germany, a typical case for a right-wing populist party exploiting anti-immigrant sentiment. Focusing on the AfD in Germany, we illustrate that the refugee crisis in 2015 in combination with a perception of high government unresponsiveness to stop the crisis provided the structural conditions necessary to activate latent anti-immigration sentiment among large parts of the population. Using a structural analysis and individual panel data for Germany’s general elections in 2013 and 2017, we find that immigration critical attitudes were already present among parts of the population in 2013 but immigration was a secondary topic in the 2013 election, even among AfD voters. Due to the immigration crisis in 2015, immigration became a salient topic. The combination of a perceived external crisis or shock combined with a perceived government’s unresponsiveness quickly offered a winning formula for the AfD. A probability probe for two other countries (Sweden and Italy) with different contexts also show salience for the model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".