Mapping the heterogeneity of political beliefs and rigidity
Bibliographic record
Abstract
Longstanding psychological accounts of political conservatism and political extremism have emphasized the critical role of rigid thoughts, feelings, goals, and behaviors. However, these theories rest on a set of auxiliary, implicit hypotheses that can be broadly summarized as follows: rigidity, conservatism, and extremism are conceptually and statistically coherent and offer an appropriate level of definitional resolution for researchers to characterize their mechanistic interplay. Yet, rigidity and political beliefs are heterogeneous phenomena and so, too, may be their channels of covariance. Here, we use a far-reaching set of self-report measures and cognitive tasks and a flexible, bottom-up analytic strategy to (1) explore the latent structure of political beliefs and rigidity, broadly construed, (2) identify relations between various “flavors” of belief systems and rigidity, and (3) identify dimension-specific non-linear effects (N = 850, demographic quota-matched U.S. sample). The result is a high-resolution and high-bandwidth “map” of the covariance space that challenges several core assertions of popular theoretical models of political ideology and extremism and surfaces several novel hypotheses that merit consideration in future research. Our results underscore the degree to which individual differences (in both politics and rigidity) are situated within intricate causal systems and manifest heterogeneously across people and places.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".