Science beliefs, political ideology, and cognitive sophistication
Bibliographic record
Abstract
It is often assumed that a primary source of contention surrounding science is political and, therefore, that partisan disagreement drives attitudes about various science topics. Other models focus on the roles of basic science knowledge and cognitive sophistication, arguing that these facilitate pro-science beliefs. To test these competing accounts, we identified a range of controversial issues ostensibly subject to potential ideological disagreement and examined the relative roles of political ideology, science knowledge, and cognitive sophistication. Results show there was actually very little partisan disagreement on a wide range of nonetheless contentious scientific issues. We also found only weak evidence for identity-protective cognition; instead, reasoning ability was broadly associated with pro-science beliefs. Two experiments that focused specifically on anthropogenic climate change found that increasing political motivations did not increase polarization among individuals who are higher in cognitive sophistication, indicating that increasing political motivations may not have as straightforward of an impact in this context as often assumed. Finally, one’s level of basic science knowledge was the most consistent predictor of people’s beliefs about science across a wide range of issues. Results suggest that educators and policymakers should focus on increasing basic science literacy and critical thinking rather than the ideologies that purportedly divide people.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".