A global scale of economic left-right party positions: cross-national and cross-expert perceptions of party placements
Bibliographic record
Abstract
We examine the cross-national comparability of expert placements of political partieson the economic left-right dimension using a novel dataset combining data from Europe, Latin America, Australia, Israel, Canada, and the United States. Using anchoring vignettes and Bayesian Aldrich-McKelvey Scaling (BAM), we assess evidence of geographic and expert-level differential item functioning (DIF) in how experts interpret the left-right scale. We find statistically significant but substantively small variations in how experts perceive party positions cross-nationally, particularly in terms of directional bias and the spread of their ideological placements. While the correlation between “raw” survey scores and DIF-corrected estimates is high (0.992), we observe meaningful deviations for individual parties, with larger discrepancies between rather than within regions. These results indicate that the economic left-right dimension exhibits broad consistency in expert understanding across countries, yet researchers should still exercise caution when making cross-national comparisons, particularly across regions where expert perceptions show greater variation.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".