Unnecessarily divided: Civil conversations reduce attitude polarization more than people expect.
Bibliographic record
Abstract
People with opposing attitudes can learn from one another through civil discourse and debate. Yet, people routinely avoid discussing their differences of opinion, preferring instead to discuss their attitudes with like-minded others. We propose that people lack interest in discussing their differences of opinion, in part, because they expect such conversations are unlikely to change their own and others' attitudes. Importantly, we find these expectations are systematically miscalibrated: Civil conversations reduce attitude polarization more than people anticipate. Participants with opposing attitudes toward cats and dogs (Study 1 and Supplemental Study S1), cancel culture (Studies 2 and 4), and Joe Biden's performance as president (Study 5) underestimated how much their own and others' attitudes would depolarize in spoken conversations. Moreover, participants retained somewhat less polarized attitudes 1 week later. Participants underestimated attitude change, because they misunderstood why their attitudes differed: Whereas participants inferred their attitudes differed, because they fundamentally disagreed; their attitudes actually differed, because they were focused on different aspects of these topics (Study 3). As such, having a conversation surfaced unexpected areas of agreement (Studies 2, 4, and 5). Importantly, participants became more interested in discussing their differences of opinion, when they were informed that their own and others' attitudes might depolarize in a conversation (Study 6 and Supplemental Study S2). In total, the current work reveals that miscalibrated expectations can create an unnecessary barrier to civil discourse, leaving people with diverse points of view more divided, more polarized, and less informed than they otherwise could be. (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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".