Bibliographic record
Abstract
During the height of the Mughal Empire in pre-colonial South Asia (16th–17th century CE), Muslim nobles facilitated the translation of numerous Hindu Sanskrit texts into the Persian language. While this “translation movement” (Ernst, 2003: 173) had long been attributed to the reputedly liberal, tolerant, and enlightened personal inclinations of the Mughal emperors, scholars in recent decades have begun to re-evaluate the phenomenon, arguing instead that practical socio-political considerations and quotidian cultural processes best explain the nature of the translation movement. What such analyses lack, however, is a sustained consideration of how the Islamic – and, in particular, Sufi – worldview(s) of the nobles in question shaped the inner workings of, and motivations behind, the movement. In this essay, I take up one such translation from the Mughal period – Mir Findiriski’s Muntakhab-i Jug Basisht, a translation of the Sanskrit Laghu-Yoga-Vasistha – examining not only its content in relation to the Sanskrit original, but also the manner in which Sufi thought and metaphysics informed the very process of translation itself.
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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 | 0.059 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".