Underestimating Counterparts’ Learning Goals Impairs Conflictual Conversations
Bibliographic record
Abstract
Given the many contexts in which people have difficulty engaging with views that disagree with their own—from political discussions to workplace conflicts—it is critical to understand how conflictual conversations can be improved. Whereas previous work has focused on strategies to change individual-level mindsets (e.g., encouraging open-mindedness), the present study investigated the role of partners’ beliefs about their counterparts. Across seven preregistered studies ( N = 2,614 adults), people consistently underestimated how willing disagreeing counterparts were to learn about opposing views (compared with how willing participants were themselves and how willing they believed agreeing others would be). Further, this belief strongly predicted greater derogation of attitude opponents and more negative expectations for conflictual conversations. Critically, in both American partisan politics and the Israeli-Palestinian conflict, a short informational intervention that increased beliefs that disagreeing counterparts were willing to learn about one’s views decreased derogation and increased willingness to engage in the future. We built on research recognizing the power of the situation to highlight a fruitful new focus for conflict research.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".