Gender, Religion and Partition: The Shifting Sītā in Chandra Prakash Dwivedi's Pinjar
Bibliographic record
Abstract
My paper aims to negotiate the political illustration of the pure Hindu woman as propagated during the India-Pakistan Partition of 1947. The split of British India was followed by communal violence and the mass abduction of women from both sides of the Indo-Pakistan border. Amid the wave of sectarian belligerence, the abducted Hindu woman was popularly classified as Sita from the Rāmāyaṇa, who was held captive by the diabolical enemy or ‘Muslim Ravana.’ I examine how religious narratives during the Partition era endorsed a reductionist dichotomy of India-Pakistan, Hindu-Muslim, and the juxtaposed iconographies of the Hindu Sita and the Muslim Ravana. In tracing the dialogue on Hinduism, gender, and the nation in the 2003 Period Drama film Pinjar, I offer insights into ways in which the film contests religious/religio-national gendered subjects by portraying hybrid spaces, liminal identities, and psychically fluid boundaries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".