How Legacies of Geopolitical Trauma Shape Popular Nationalism Today
Bibliographic record
Abstract
Geopolitical competition and conflict play a central role in canonical accounts of the emergence of nation-states and national identities. Yet work in this tradition has paid little attention to variation in everyday, popular understandings of nationhood. We propose a macro-historical argument to explain cross-national variation in the types of popular nationalism expressed at the individual level. Our analysis builds on recent advances on the measurement of popular nationalism and a recently introduced geopolitical threat scale (Hiers, Soehl, and Wimmer 2017). With the use of latent class analysis and a series of regression models, we show that a turbulent geopolitical past decreases the prevalence of liberal nationalism (pride in institutions, inclusive boundaries) while increasing the prevalence of restrictive nationalism (less pride in institutions, exclusive boundaries) across 43 countries around the world. Additional analyses suggest the long-term development of institutions is a key mediating variable: states with a less traumatic geopolitical history tend to have more established liberal democratic institutions, which in turn foster liberal forms of popular nationalism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".