Variations of science-related populism in comparative perspective: A multilevel segmentation analysis of supporters and opponents of populist demands toward science
Bibliographic record
Abstract
Many countries worldwide have seen populist resentment against scientists, which can manifest as “science-related populist attitudes” among the population. These attitudes can be assumed to divide populations into multiple segments—each endorsing or rejecting different facets of science-related populism, with segment sizes and characteristics varying between countries and cultural contexts. This study tests this with a secondary analysis of four public opinion surveys from Austria, Germany, Switzerland, and Taiwan (total N = 4598), combining a Most Similar Systems Design (MSSD) and a Most Different Systems Design (MDSD). It uses fixed-effects latent class analysis to demonstrate that Austrian, German, Swiss, and Taiwanese publics can be grouped into three segments: Full-Fledged Populists, People-Centric Non-Populists, and Deferent Anti-Populists. A large majority in all countries can be classified as Non-Populist or Anti-Populists, whereas Populists, who support the entire spectrum of science-related populism, make up the smallest segment. Bayesian regression shows that Populists are older and more likely to support right-leaning political views. Cross-country and cross-cultural comparisons reveal differences in segment sizes and characteristics: For example, Populists are more prevalent in Austria, while Germany has a large proportion of Anti-Populists. These are less widespread in Taiwan, where Non-Populists form a particularly big segment. The findings can be explained with national political, cultural, and historical contexts to some degree. Eventually, they are discussed against the backdrop implications for science communication and future scholarship on public science skepticism.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".