Bibliographic record
Abstract
While early canonical Jain literature may well justify the assessment that some scholars have made about the Jains’ stoic resistance to medical aid, later post-canonical Śvetāmbara Jain texts reveal in fact a much more complex relationship to practices of healing. They make frequent references to medical practice and the alleviation of sickness, describing various medical procedures and instruments and devoting long sections to the interaction between doctors and monastics as issues that a monastic community would have to negotiate as a matter of course. The amount of medical knowledge — indeed fascination with healing human ailments — evident in these later texts invites us to pause before concluding that pre-modern Jain monastic traditions were disinterested in alleviating physical distress. It seems that, on the contrary, the question of when and how to treat the sick within the community emerged as a central concern that preoccupied the monastic authorities and commentators and left its mark on the texts they compiled. Moreover, from the early medieval period onwards, Jains enter the history of Indian medical literature as authors and compilers of actual medical treatises. In what follows, I try to trace this historical shift in Śvetāmbara Jain attitudes to medicine and healing, from the early canonical texts to post-canonical commentaries on the mendicants’ rules. Specifically, I focus on the treatment of medicine in three monastic commentaries composed around the sixth and seventh centuries CE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".