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Record W2622245121 · doi:10.15388/aov.2007.1.3751

How to become a Brahman: The construction of varṇa as social place in the Mahābhārata’s legends of Viśvāmitra

2007· article· en· W2622245121 on OpenAlexaff
Adheesh Sathaye

Bibliographic record

VenueActa orientalia Vilnensia · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNarrativeEPICLiteratureBrahmanAsceticismSanskritArticulation (sociology)SociologyPoliticsPhilosophyLawArtTheologyPolitical scienceBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.324
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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