The role of political devotion in sharing partisan misinformation and resistance to fact-checking.
Bibliographic record
Abstract
= 36). Far-right partisans in Spain and U.S. Republicans who highly identify with Trump were more likely to share misinformation than center-right voters and other Republicans, especially when the misinformation was related to sacred values (e.g., immigration). Sacred values predicted misinformation sharing above and beyond familiarity, attitude strength, and salience of the issue. Moreover, far-right partisans were unresponsive to fact-checking and accuracy nudges. At a neural level, this group showed increased activity in brain regions implicated in mentalizing and norm compliance in response to posts with sacred values. These results suggest that the two components of political devotion-identity fusion and sacred values-play a key role in misinformation sharing, highlighting the identity-affirming dimension of misinformation sharing. We discuss the need for motivational and identity-based interventions to help curb misinformation for high-risk partisan groups. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".