Impersonations: The Artifice of Brahmin Masculinity in South Indian Dance
Bibliographic record
Abstract
Impersonations centers on an insular community of Smarta brahmin men from the Kuchipudi village in Telugu-speaking South India who don strī-vēṣam (woman’s guise) and impersonate female characters from Hindu religious narratives. Impersonation is not simply a gender performance limited to the Kuchipudi stage, but a practice of power that enables the construction of hegemonic brahmin masculinity in everyday village life. This book analyzes the practice of impersonation across a series of boundaries—village to urban to transnational, brahmin to non-brahmin, hegemonic to nonnormative—to explore the artifice of brahmin masculinity in contemporary South Indian dance. “By charting the complex practices of masculinity through South Indian dance, Kamath deconstructs hegemonic brahmin masculinity and offers an intellectual tour de force that challenges Western epistemologies of gender.” STANLEY THANGARAJ, author of Desi Hoop Dreams: Pickup Basketball and the Making of Asian American Masculinity “Beautifully crafted and presented, this book fills a lacuna in scholarship on the figure of the brahmin man in relation to his gender identity and makes a compelling contribution to the field of Indian dance historiography, which often overlooks the critical role the dancing male body.” VASUDHA NARAYANAN, Distinguished Professor of Religion, University of Florida HARSHITA MRUTHINTI KAMATH is Visweswara Rao and Sita Koppaka Assistant Professor in Telugu Culture, Literature and History at Emory University.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".