Locating the<i>Tawa’if</i>Courtesan-Dancer: Cinematic Constructions of Religion and Nation
Bibliographic record
Abstract
The development of the Hindi/Urdu cinema is intimately connected to the history of artistic performance in India in two important ways. Not only did hereditary music and dance practitioners play key roles in building this cinema, representations of these performers and their practices have been, and continue to be, the subject of Indian film narratives, genres, and tropes. I begin with this history in order to explore the Muslim religio-cultural and artistic inheritance that informs Hindi/Urdu cinema, as well as examine how this heritage has been incorporated into the cinematic narratives that help construct distinct gendered, religious, and national identities. My specific focus is on the figure of the tawa’if dancer, often equated with North Indian culture and nautch dance performance. Analyzing the ways in which traces of the tawa’if appear in two recent films, Dedh Ishqiya and Begum Jaan, I show how this figure is placed in a larger representational regime that sustains nationalist formations of contemporary Indian identity. As I demonstrate, even in the most blatant attempts to define the Indian nation as “Hindu,” the “Muslimness” of the tawa’if—and by extension the cinema she informed in ways both real and representational—is far from relinquished.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".