The three facets of national identity: Identity dynamics and attitudes toward immigrants in Russia
Bibliographic record
Abstract
This study contributes to the discussion on individual-level determinants of anti-immigrant prejudice by applying a multi-dimensional model of manifestations of national identity in Russia. This context is particularly interesting as anti-immigrant sentiments are widespread across all social strata and thus, socioeconomic indicators and political views are weak predictors of such sentiments. We use cross-sectional data from 1995, 2003, and 2013 ISSP National Identity module to assess the dynamics of three facets of national identity in Russia, namely nationalism, political patriotism, and cultural patriotism, and their relations with attitudes toward immigrants. We find nationalism, political patriotism, and anti-immigrant attitudes to increase over time. More importantly, our findings support the theoretical distinction between the facets of national identity: nationalism is linked to anti-immigrant attitudes, political patriotism is linked to more positive attitudes, and cultural patriotism is largely unrelated to attitudes toward immigrants. We show that these facets of national identity have much higher predictive power than sociodemographic indicators or political views. Our findings underscore the utility of a nuanced assessment of national identity in explaining attitudes toward immigrants in non-Western contexts.
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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 | 0.000 |
| 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 | 0.003 |
| 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".