Bibliographic record
Abstract
Buddhist literature in Pāli presents a world that is rich in animal imagery, with some animals carrying largely positive associations and other animals seen in a consistently negative light. Among the many species that populate the Pāli imaginaire, the jackal bears a particular status as a much-maligned beast. Jackals are depicted in Pāli literature as lowly, inferior, greedy, and cunning creatures. The jackal, as a natural scavenger, exists on the periphery of both human and animal society and is commonly associated with carrion, human corpses, impurity, and death. In this paper, I am interested in the use of the jackal as an image for both heresy and heterodoxy—that is, the jackal’s consistent association with heretical Buddhist figures, such as Devadatta, and with heterodox teachers, such as the leaders of competing samaṇa movements. Why was the jackal such an appropriate animal to stand for those who hold the wrong views? And how does association with such an animal sometimes result in a particularly nefarious sort of dehumanization that goes against the teachings of Buddhism?
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".