From Selective Exposure to Mobilization: Partisan Media, Polarization, and Voting Behaviors
Bibliographic record
Abstract
ABSTRACT Objective This article aims to extend our understanding of relationships among partisan selective exposure, polarization, and voting behavior. Specifically, this research deals with the different types of polarization (affective polarization and perceived polarization) and electoral participation (turnout, vote choice, and lateness of decision). Methods A series of path analyses were performed using the American National Election Survey (ANES) 2020. Results The results show that pro‐attitudinal media use is positively associated with affective polarization, while counter‐attitudinal media use is negatively associated with affective polarization. In addition, people with an exaggerated perception of polarization are likely to experience greater affective polarization. Both affective polarization and perceived polarization are positively associated with turnout, while only heightened affective polarization shortens the time it takes to decide who to vote for. Conclusion Affective polarization and perceived polarization, amplified or dampened by partisan media use, are distinctly related to different forms of voting behaviors. This finding calls for a more nuanced approach to explain partisan audiences and their political participation in a fragmented era.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".