Moralization and Amoralization Predict Empathy and Perceptions of Bias in Contentious Social Domains
Bibliographic record
Abstract
A dominant view is that moralized attitudes—attitudes rooted in moral values rather than personal taste—escalate social conflict. While moralized attitudes clearly contribute, another view is that societal conflict is perpetuated by a dynamic tension between those with moralized attitudes and those with amoralized attitudes—attitudes divested of moral relevance. Three studies investigated whether both moralized and amoralized attitudes heighten conflict-escalating interpersonal responses (i.e. low empathy, high perceived bias). In each study participants reported how much they moralize a social issue (e.g. abortion rights) and imagined interacting with someone who opposed their stance. All studies found that high-moralizers (those at the high end of the moralization scale) and low-moralizers (those at the low end) expressed less empathy and perceived more bias, compared to moderate moralizers. Studies 2 and 3 investigated psychological mechanisms. Moral convictions—the personal belief that an issue is moral or immoral—mediated the conflict-escalating responses of high-moralizers, but not low-moralizers, and moral aversion—a dislike of moralized social-political discourse—mediated the conflict-escalating responses of low-moralizers, but not high-moralizers. Results demonstrate that both moralized and amoralized attitudes can escalate social conflict and reveal the importance of attitude amoralization in understanding social and political conflict.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".