Why Won’t You Listen to Me? Measuring Receptiveness to Opposing Views
Bibliographic record
Abstract
We develop an 18-item self-report measure of receptiveness to opposing views. Studies 1a and 1b present the four-factor scale and report measures of internal, convergent, and discriminant validity. In study 2, more receptive individuals chose to consume proportionally more information from U.S. senators representing the opposing party than from their own party. In study 3, more receptive individuals reported less mind wandering when viewing a speech with which they disagreed, relative to one with which they agreed. In study 4, more receptive individuals evaluated supporting and opposing policy arguments more impartially. In study 5, we find that voters who opposed Donald Trump but reported being more receptive at the time of the election were more likely to watch the inauguration, evaluate the content of the inauguration speech in a more even-handed manner, and select a more balanced portfolio of news outlets for later consumption than their less receptive counterparts. We discuss the scale as a tool to investigate the role of receptiveness for conflict, decision making, and collaboration. This paper was accepted by Elke Weber, judgment and decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".