Contrasting perspectives: Belief in national superiority in relation to countries’ performance
Bibliographic record
Abstract
This article examines cross-country differences in the strength of individuals’ belief that their country is better than most others and the dependence of this belief on their country’s performance in various spheres. The research design consists of a series of multilevel ordinal logistic regression models estimated using the data of the most recent thematic wave of the International Social Survey Programme (ISSP)—National Identity module. Our research finds that these effects are mostly nonlinear U-shaped: people from both high- and low-performing countries express a strong belief in their country’s superiority, while people from average-performing countries do not. These findings suggest a bifurcated nature of belief in national superiority—an interplay between a grounded estimation of a country’s actual achievements and the social norms and individual motivations that prescribe holding one’s own country in high esteem regardless of its actual performance. These norms are found to be the strongest in underperforming countries, while in average- and high-performing countries, people making these evaluations are under weaker normative pressure and therefore more attuned to country achievements. As a result, the weakest belief in national superiority is found not in underperforming, but in average-performing countries. The latter also have the highest diversity on this belief, probably because different segments of the population compare their country’s performance against different benchmarks.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".