Can nationalism and group conflict explain cultural and economic threat perceptions? Cross-sectional and longitudinal evidence from the ISSP (1995–2013)
Bibliographic record
Abstract
This article examines how nationalism, together with group conflict factors (namely, immigrant group size and economic conditions), affects ethnic threat perceptions over a period of almost 20 years across European and non-European countries. For this purpose, we analyze three rounds (1995, 2003, and 2013) of the International Social Survey Program (ISSP) National Identity Module using societal growth curve models. Our findings contribute to the ongoing discussion on the contextual drivers and dynamics of threat perceptions in various ways. First, our models show that nationalism is a highly relevant factor in explaining cultural as well as economic threats. However, nationalist attitudes operate purely at the individual level, as no effect of the group-level aggregate of nationalism is found. Second, the growth curve models make it possible to disentangle longitudinal effects (describing how threat perceptions evolve within countries) from cross-sectional patterns (describing the stable differences between countries). The longitudinal effects of group conflict variables deviate from the cross-sectional effects and are mostly insignificant. Given that these longitudinal effects are the litmus test for a causal interpretation, we must conclude that we find little to no evidence for the dynamic claims of group conflict theory. Finally, we detect an interaction between nationalism and labor market conditions: The impact of unemployment rates on threat perceptions is found to be contingent on the nationalist attitudes of individuals.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".