Science beliefs, political ideology, and cognitive sophistication.
Bibliographic record
Abstract
Some theoretical models assume that a primary source of contention surrounding science belief is political and that partisan disagreement drives beliefs; other models focus on basic science knowledge and cognitive sophistication, arguing that they facilitate proscientific beliefs. To test these competing models, we identified a range of controversial issues subject to potential ideological disagreement and examined the roles of political ideology, science knowledge, and cognitive sophistication on science beliefs. Our results indicate that there was surprisingly little partisan disagreement on a wide range of contentious scientific issues. We also found weak evidence for identity-protective cognition (where cognitive sophistication exacerbates partisan disagreement); instead, cognitive sophistication (i.e., reasoning ability) was generally associated with proscience beliefs. In two studies focusing on anthropogenic climate change, we found that increased political motivations did not increase polarization among individuals who are higher in cognitive sophistication, which indicates that increased political motivations might not have as straightforward an impact on science beliefs as has been assumed in the literature. Finally, our findings indicate that basic science knowledge is the most consistent predictor of people's beliefs about science across a wide range of issues. These results suggest that educators and policymakers should focus on increasing basic science literacy and critical thinking rather than on the ideologies that purportedly divide people. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".