Bibliographic record
Abstract
The relationship between morality and disgust has been a matter of considerable interest and debate in modern scholarship, while its salience in premodern religious history still deserves more attention. This article focuses on the role of disgust in medieval Muslim thought, identifying relevant notions in classical Arabic and examining their usage in traditionist sources from the first few centuries of Islam (seventh to tenth centuries ce). Hadith narratives illustrate the concept of taqadhdhur or revulsion to a disgusting object, especially in the context of food. Well-known accounts of the Prophet Muhammad’s aversion to an Arab custom of eating lizards raised questions about the normative implications of his disgust, which medieval jurists sought to resolve through interpretive debates on the category of khaba'ith (disgusting things) in dietary law. In contrast to the circumstantial role of disgust in law, appeals to disgust served as a rhetorical strategy for moral persuasion against sin in the literature of hadith, Qur'anic exegesis, and traditions associated with renunciant piety (zuhd). A paradigmatic case is the invocation of a particular motif, the rotting corpse (jifa), which is considered to be a core elicitor of disgust and was frequently used in early Islamic sources as a trope for the vice of gossip or backbiting (ghiba). The metaphor was elaborated through traditions centered on the sensory dimensions of disgust as a bodily phenomenon. Stories of pious Muslims suggest the affective power of such traditions to inculcate revulsion toward sin. I argue that early Muslim pietists regarded disgust as a didactic instrument and a morally productive emotion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".