The structure of foreign policy attitudes in transatlantic perspective: Comparing the United States, United Kingdom, France and Germany
Bibliographic record
Abstract
Abstract While public opinion about foreign policy has been studied extensively in the United States, there is less systematic research of foreign policy opinions in other countries. Given that public opinion about international affairs affects who gets elected in democracies and then constrains the foreign policies available to leaders once elected, both comparative politics and international relations scholarship benefit from more systematic investigation of foreign policy attitudes outside the United States. Using new data, this article presents a common set of core constructs structuring both American and European attitudes about foreign policy. Surveys conducted in four countries (the United States, the United Kingdom, France and Germany) provide an expanded set of foreign policy‐related survey items that are analysed using exploratory structural equation modeling (ESEM). Measurement equivalence is specifically tested and a common four‐factor structure that fits the data in all four countries is found. Consequently, valid, direct comparisons of the foreign policy preferences of four world powers are made. In the process, the four‐factor model confirms and expands previous work on the structure of foreign policy attitudes. The article also demonstrates the capability of ESEM in testing the dimensionality and cross‐national equivalence of social science concepts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".