Rasāyana and Rasaśāstra in the Persian Medical Culture of South Asia
Bibliographic record
Abstract
In this article, I suggest that looking at the entangled issues of the creation of a new field of knowledge and the interaction with Others’ learning allows for a more accurate understanding of how Persian medical studies have developed and adapted to different natural and cultural settings during late medieval and early modern periods. This article studies the translation and reception of materials drawn from alchemy (rasaśāstra) and rejuvenating therapy (rasāyana) in the Persianate medical culture of South Asia. Chapters dealing with processed mercury and metals become a standard subject of Persian medical works written by Muslim and Hindu physicians in South Asia. Many of these works are in fact composite writings which combine Ayurvedic and Greco-Arabic materials. However, rasāyana is a branch of knowledge for which there is not a precise equivalent domain in the target culture. How does translation deal and negotiate with this asymmetry? In this study, I assume that cross-cultural translation implies a cognitive shift in the way different groups of readers may understand and classify a certain form of knowledge. I look at the Persian translation of materials drawn from rasāyana chiefly from the reader perspective which focuses on the hermeneutical and accommodation process through which translated materials are integrated into the target culture.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".