Asymmetric polarization: The perception that Republicans pose harm to disadvantaged groups drives Democrats’ greater dislike of Republicans in social contexts.
Bibliographic record
Abstract
= 2,443) operationalizing partisan dislike in various ways (e.g., blocking on social media, rating the likability of various targets, and evaluating hiring suitability)-that Democrats (i.e., liberals) dislike Republicans (i.e., conservatives) more than vice versa. We provide a potential explanation for this phenomenon by extending the worldview conflict perspective to account for asymmetries in how moralized specific values are among two conflicting groups at a given point in time. Specifically, we theorize that in light of recent social trends in the modern-day United States, the moralized belief that counter-partisans pose harm to disadvantaged groups, particularly racial/ethnic minorities, has become an asymmetric contributor to partisan dislike among Democrats. We found support for our theory across both measurement-of-mediation and experimental-mediation approaches and in both field experimental and survey data. Overall, this work advances research on ideology and outgroup hostility and extends the worldview conflict perspective to better explain partisan dislike. (PsycInfo Database Record (c) 2026 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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".