Visions and Revisions of the Hindu Goddess: Sound, Structure, and Artful Ambivalence in the Devī Māhātmya
Bibliographic record
Abstract
The Hindu Goddess makes her Brahmanical debut circa 5th century CE in the Sanskrit narrative work Devī Māhātmya, the “Greatness of the Goddess” (henceforth DM). This monumental mythic moment enshrines the first Indic articulation of ultimate divinity as feminine. That she is perennially feminine and ever omnipotent, there can be no doubt. But how do we further characterize this feminine face? This study performs a close synchronic examination of the DM to demonstrate the extent to which it encodes an ambivalence on behalf of the Devī (Goddess) between violent wrath and compassionate care. Preserving paradox as only narrative can, the DM dispenses with neither face of the supreme Goddess—yet it posits her benign visage as ultimately supreme. This paper firstly examines the use of sound throughout the DM as expressive of the Devī’s sacrality and virulence alike. While violent sound is something the Devī deploys, sacred sound is something the Devī is. It then proceeds to analyze the second of the four hymns within the DM—the Śakrādi Stuti, occupying Chapter 4—to demonstrate the artful manner in which the hymn encodes the Devi’s ambivalence through its sophisticated design. This paper ultimately suggests that this ambivalence of the Devī finds an earthly analogue in the Indian king.
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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 | 0.012 | 0.029 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".