Moral reframing: A technique for effective and persuasive communication across political divides
Bibliographic record
Abstract
Abstract The political landscape in the US and many other countries is characterized by policy impasses and animosity between rival political groups. Research finds that these divisions are fueled in part by disparate moral concerns and convictions that undermine communication and understanding between liberals and conservatives. This “moral empathy gap” is particularly evident in the moral underpinnings of the political arguments members of each side employ when trying to persuade one another. Both liberals and conservatives typically craft arguments based on their own moral convictions rather than the convictions of the people they target for persuasion. As a result, these moral arguments tend to be unpersuasive, even offensive, to their recipients. The technique of moral reframing —whereby a position an individual would not normally support is framed in a way that is consistent with that individual's moral values—can be an effective means for political communication and persuasion. Over the last decade, studies of moral reframing have shown its effectiveness across a wide range of polarized topics, including views of economic inequality, environmental protection, same‐sex marriage, and major party candidates for the US presidency. In this article, we review the moral reframing literature, examining potential mediators and moderators of the effect, and discuss important questions that remain unanswered about this phenomenon.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".