Three Dimensions of American Conservative Political Orientation Differentially Predict Negativity Bias and Satisfaction With Life
Bibliographic record
Abstract
Numerous studies have linked political conservatism with negativity bias, whereas others have linked conservatism with indicators of positive adjustment. This research sought to reconcile this seeming contradiction by examining whether distinct dimensions of conservatism differentially predicted measures of negativity bias and positive adjustment. In two studies, we used an empirically derived and validated Attitude-Based Political Conservatism (ABPC) Scale that captures three correlated but distinct factors of American conservatism: Libertarian Independence, Moral Traditionalism, and Ethnic Separateness. In both studies ( N = 1,756), Libertarian Independence was linked with indicators of positive adjustment, whereas Moral Traditionalism and Ethnic Separateness were linked with indices of negativity bias. By identifying which dimensions of conservatism predict negativity bias and positive adjustment, this work illuminates the unique psychological foundations of distinct strands of conservatism in America.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".