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Record W3181343168 · doi:10.1093/jhs/hiab014

Reading the Mahābhārata as Śāstra: The Role of the ‘Righteous’ Pāṇḍavas and ‘Villainous’ Kauravas in Madhva's Dvaitavedānta

2021· article· en· W3181343168 on OpenAlexaff
Vishal Sharma

Bibliographic record

VenueThe Journal of Hindu Studies · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNarrativeExegesisPhilosophyHeavenLiteratureEpistemologyBattleTheologyMeaning (existential)ArtLinguisticsHistory

Abstract

fetched live from OpenAlex

Abstract The Sanskrit Mahābhārata is considered to be a śāstra in Mādhva Vaiṣṇavism, and Madhva composed one of the earliest treatises dedicated to explaining the Mahābhārata’s deeper meaning. In doing so, he pays careful attention to the distinction between the righteous Pāṇḍavas and corrupt Kauravas—a distinction that exists in theory if not always in practice. How does Madhva demarcate the Kauravas and the Pāṇḍavas, and why does this matter for his own theology? Looking at two episodes that either blur or upend the demarcation of the Kauravas and Pāṇḍavas, the mace-battle between Bhīma and Duryodhana (9.60) and the Pāṇḍavas’ journey to heaven (17-18.3), this article shows how Madhva’s Mahābhāratatātparyanirṇaya explains key tenets of Madhva’s ontology and theology. When read carefully, each episode brings out important features of Madhva’s doctrine of devatātāratamya—the gradation of gods. Rather than simply understanding Madhva’s commentary as a theologisation or vedānticisation of the narrative, I argue that his exegesis itself develops and explains central doctrines of Dvaitavedānta, particularly tāratamya and aparokṣajñāna. Vedānticization, in this understanding then, is not simply bringing forth an interpretation of the narrative that fits one's prior philosophical commitments, but it is a process of constituting theology through the narrative.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
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
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.236
Teacher spread0.211 · 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.

Study designQualitative
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
Published2021
Admission routes1
Has abstractyes

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