Quantitative Analysis of the Latent Structures underlying Political Preferences, Attitudes and Values and Voter Clustering in Europe
Bibliographic record
Abstract
Claessens et al. (2020, p. 1) theorized that the foundations of political preferences could be based on evolution. Two perpetual fitness trade-offs in human evolution, relating to cooperation and norm-conformity, are reflected in two-dimensional factor spaces often found to be underlying political preferences. Therefore, they suspect them to be universal (Claessens et al., p. 2). Ashton et al. (2005) performed principal component analysis (PCA) on samples from the United States, Canada, Hong Kong, and Ghana to assess the persistence of a two-dimensional space and found structures that could be interpreted as “cooperation” and “conformity” for Canada, Wales, and Hong Kong, however not for Ghana. Research by Jones et al. (2021) provides methods to test for generalization of a factor space in another country. So far unpublished research at Cambridge (Ackland, 2022) found factors interpretable as a cooperation and a conformity dimension in Germany and the Netherlands, however not in Hungary and Italy. Furthermore, it identified voter clusters in these factor spaces. Research covered by this preregistration attempts to determine the structures underlying political preferences in Europe and assess their similarity. In a second step, voters are clustered within these structures and the traits exhibited by those clusters are compared within and across borders.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".