Morally Reframed Arguments Can Affect Support for Political Candidates
Why this work is in the frame
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Bibliographic record
Abstract
Moral reframing involves crafting persuasive arguments that appeal to the targets' moral values but argue in favor of something they would typically oppose. Applying this technique to one of the most politically polarizing events-political campaigns-we hypothesized that messages criticizing one's preferred political candidate that also appeal to that person's moral values can decrease support for the candidate. We tested this claim in the context of the 2016 American presidential election. In Study 1, conservatives reading a message opposing Donald Trump grounded in a more conservative value (loyalty) supported him less than conservatives reading a message grounded in more liberal concerns (fairness). In Study 2, liberals reading a message opposing Hillary Clinton appealing to fairness values were less supportive of Clinton than liberals in a loyalty-argument condition. These results highlight how moral reframing can be used to overcome the rigid stances partisans often hold and help develop political acceptance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| 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 | 0.000 |
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 it