A Requiem for Women: Narrative Strategy, Rhetoric, and Ideology in Candrāvatī’s Bengali <i>Rāmāyaṇa</i>
Bibliographic record
Abstract
Abstract The numerous versions of Vālmīki’s original Rāmāyaṇa in different languages and milieus display an almost limitless cultural and ideological variety. Whether purportedly faithful translations or interpretive re-creations, these versions present a vast range of responses to a narrative that has been often transformed to reflect contemporary social and spiritual experiences and positions. A particularly intriguing example of such a transformation is the late 15th c. Bengali Rāmāyaṇa of Candrāvatī, of interest not only because this is the first extant work by a female author in the tradition of Bengali poetry but more importantly because (i) she composed the narrative from a woman’s point of view, and (ii) she converted the original battle narrative into a dirge for the victims of war, especially women. In her poem the validation of war and conquest is set aside, justice is shown to be unjust, and even faith as a spiritual axiom is undermined. Candrāvatī’s literary strategy, including her diction, iterative statements and lyrical forms, replaces the heroic style of the conventional epic with the language of feminine domesticity. The Rāmāyaṇa is thus turned against itself emotionally and ideologically by being reoriented to women’s repeatedly authenticated experience of the world. My essay traces through the literary and ideological processes of Candrāvatī’s poem its assault upon the chains that bind women as victims of justice perverted in the dominant Rāma legend. Whether and how far that assault also affects the integrity of religious faith lends a speculative edge to my enquiry.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".