Disgust and Moral Judgment: Distinguishing Between Elicitors and Feelings Matters
Bibliographic record
Abstract
We investigated the scope of the effect of disgust on moral judgments. In two field experiments (Experiment 1, N = 142, Experiment 2, N = 248), we manipulated whether participants were exposed to a disgusting odor. Participants then rated the permissibility of actions in two kinds of moral problems: dilemmas and transgressions. In both experiments, disgust did not affect moral judgments when we compared across exposure levels. However, self-reported disgust did predict moral judgments in the following cases: In Experiment 1, it was linked with decreased acceptability for dilemmas and transgressions alike; in Experiment 2, it was linked with decreased acceptability for dilemmas only. Findings also differed across the experiments when we regressed feelings of disgust onto participants’ utilitarian and deontological inclinations. Overall, the findings suggest that subjective feelings of disgust may provide a more sensitive measure of the effect of disgust on moral judgment than basing analysis on the presence of disgust elicitors.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".