Maggots and morals: Physical disgust is to fear as moral disgust is to anger1
Bibliographic record
Abstract
Abstract We argue from an appraisal theory point of view that physical and moral disgust involve different appraisals and thus different experiences, physiologies, action tendencies, and motivations to regulate expression. We hypothesized that physical disgust shares attributes with fear, and moral disgust with anger. Using the GRID data, we found that moral disgust and anger are characterized by a constellation of features—most notably the attribution of agency to another person, the violation of social norms, the presence of value-laden judgments, and the urge to approach and punish. Physical disgust and fear involve no value-laden judgment, but a sense of weakness/submissiveness and an urge to avoid and comply. These findings raise new questions about the role of agency, emotional complexity, and cultural-linguistic variations in the two kinds of disgust
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".