Liberals and Conservatives are Similarly Motivated to Avoid Exposure to One Another’s Opinions
Bibliographic record
Abstract
Ideologically committed people are similarly motivated to avoid ideologically crosscutting information. Although some previous research has found that political conservatives may be more prone to selective exposure than liberals are, we find similar selective exposure motives on the political left and right across a variety of issues. The majority of people on both sides of the same-sex marriage debate willingly gave up a chance to win money to avoid hearing from the other side (Study 1). When thinking back to the 2012 U.S. Presidential election (Study 2), ahead to upcoming elections in the U.S. and Canada (Study 3), and about a range of other Culture War issues (Study 4), liberals and conservatives reported similar aversion toward learning about the views of their ideological opponents. Their lack of interest was not due to already being informed about the other side or attributable election fatigue. Rather, people on both sides indicated that they anticipated that hearing from the other side would induce cognitive dissonance (e.g., require effort, cause frustration) and undermine a sense of shared reality with the person expressing disparate views (e.g., damage the relationship; Study 5). A high-powered meta-analysis of our data sets (N = 2417) did not detect a difference in the intensity of liberals' (d = 0.63) and conservatives' (d = 0.58) desires to remain in their respective ideological bubbles.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".