A global scale of economic left-right party positions: cross-national and cross-expert perceptions of party placements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it