How to become a Brahman: The construction of varṇa as social place in the Mahābhārata’s legends of Viśvāmitra
Bibliographic record
Abstract
University of British ColumbiaThis article investigates varṇa as an embodied and spatialized social practice in the Sanskrit Mahābhārata, with a focus on the epic subnarratives of Viśvāmitra, the legendary king who became a Brahman. Adopting a post-Dumontian position that the articulation of social status is always a political act, the Mahābhārata’s treatment of Viśvāmitra is analyzed as a literary attempt to secure the social place of Brahmanhood in post-Mauryan India. Two specific narratives are taken up for comparative study: first the kāmadhenu legend—the squabble with Vasiṣṭha that led to Viśvāmitra’s Brahmanhood—and then an altogether different story in which a mixup by Viśvāmitra’s sister Satyavatī meant that he had always been a Brahman by birth. Two distinct interpretive voices are heard in the same epic—one extolling Viśvāmitra’s extraordinary ascetic power, and another, louder one minimizing his realworld impact by insisting that his varṇa change never actually happened. Developing the concept of ‘textual performance’ to explain how fluid legendary material was embedded into the fixed epic corpus, this article argues that the Mahābhārata utilized counter-normative figures like Viśvāmitra to articulate alternative voices and possibilities, but within a carefully regulated epic storyworld that naturalized varṇa as an everyday social practice.
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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 | 0.001 | 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 | 0.015 | 0.023 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".