Liberals and conservatives rely on common moral foundations when making moral judgments about influential people.
Bibliographic record
Abstract
Do liberals and conservatives have qualitatively different moral points of view? Specifically, do liberals and conservatives rely on the same or different sets of moral foundations-care, fairness, loyalty, authority, and purity (Haidt, 2012)-when making moral judgments about influential people? In Study 1, 100 experts evaluated the impact that 40 influential figures had on each moral foundation, yielding stimulus materials for the remaining studies. In Study 2, 177 American liberal and conservative professors rated the moral character of the same figures. Liberals and conservatives relied on the same 3 moral foundations: For both groups, promoting care, fairness, and purity-but not authority or loyalty-predicted moral judgments of the targets. For liberals, promoting authority negatively predicted moral judgments. Political ideology moderated the purity-moral and especially authority-moral relationships, implying that purity and authority are grounds for political disagreement. Study 3 replicated these results with 222 folk raters. Folk liberals and conservatives disagreed even less about the moral standing of the targets than did experts. Together, these findings imply that moral foundation theory may have exaggerated differences between liberals and conservatives. The moral codes of liberals and conservatives do differ systematically; however, their similarities outweigh their differences. Liberals and conservatives alike rely on care, fairness, and purity when making moral judgments about influential people.
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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 | 0.003 | 0.015 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| 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".