National Trauma and the Fear of Foreigners: How Past Geopolitical Threat Heightens Anti-Immigration Sentiment Today
Bibliographic record
Abstract
This paper introduces a historical, macro-political argument into the literature on anti-immigration sentiment, which has mainly considered individual-level predictors such as education or social capital as well as country-level factors such as fluctuations in labor market conditions, changing composition of immigration streams, or the rise of populist parties. We argue that past geopolitical competition and war have shaped how national identities formed and thus also contemporary attitudes toward newcomers: countries that have experienced more violent conflict or lost territory and sovereignty developed ethnic (rather than civic) forms of nationalism and thus show higher levels of anti-immigration sentiment today. We introduce a geopolitical threat scale and score 33 European countries based on their historical experiences. Two anti-immigration measures come from the European Social Survey. Mixed-effects, ordinal logistic regression models reveal strong statistical and substantive significance for the geopolitical threat scale. Furthermore, ethnic forms of national identification do seem to mediate this relationship between geopolitical threat and restrictionist attitudes. The main analysis is robust to a wide variety of model specifications, to the inclusion of all control variables known to affect anti-immigration attitudes, and to a series of alternative codings of the geopolitical threat scale.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".