The Association of Cognitive Ability with Right–Wing Ideological Attitudes and Prejudice: A Meta–Analytic Review
Bibliographic record
Abstract
The cognitive functioning of individuals with stronger endorsement of right–wing and prejudiced attitudes has elicited much scholarly interest. Whereas many studies investigated cognitive styles, less attention has been directed towards cognitive ability. Studies investigating the latter topic generally reveal lower cognitive ability to be associated with stronger endorsement of right–wing ideological attitudes and greater prejudice. However, this relationship has remained widely unrecognized in literature. The present meta–analyses revealed an average effect size of r = −. 20 [95% confidence interval (95% CI) [−0.23, −0.17]; based on 67 studies, N = 84 017] for the relationship between cognitive ability and right–wing ideological attitudes and an average effect size of r = −.19 (95% CI [−0.23, −0.16]; based on 23 studies, N = 27 011) for the relationship between cognitive ability and prejudice. Effect sizes did not vary significantly across different cognitive abilities and sample characteristics. The effect strongly depended on the measure used for ideological attitudes and prejudice, with the strongest effect sizes for authoritarianism and ethnocentrism. We conclude that cognitive ability is an important factor in the genesis of ideological attitudes and prejudice and thus should become more central in theorizing and model building. Copyright © 2015 European Association of Personality Psychology
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".