Perceived Political Party Divide During the Pandemic: A Framework Linking Attitude Strength Toward COVID-19 Vaccines to Affective Polarization
Bibliographic record
Abstract
Abstract This study examines how perceived political party divide may foster affective polarization during the COVID-19 pandemic. Drawing on nationally representative sample surveys of U.S. and South Korean adults, we investigate the relationship between attitude strength toward COVID-19 vaccines, perceived polarization, and affective polarization. Results suggest that individuals with stronger attitudes toward vaccines are more likely to perceive a greater ideological divide between political parties, which in turn, is associated with increased animosity toward the opposing party. Applying the mediation model to the South Korean sample yielded consistent findings, suggesting that the mechanism by which strong attitudes relate to negative emotions through perceived polarization may be generalizable across countries with a competitive two-party system, particularly in the context of COVID-19 vaccination attitudes. Our findings highlight perceived polarization as an important intermediary in the process of affective polarization, shedding light on the mechanisms driving partisan hostility in contemporary democracies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".