Men in Mohiniyattam: An Ethnographic Study on Gender Binaries
Bibliographic record
Abstract
Mohiniyattam is an Indian Classical Dance form that originated in the state of Kerala and is popularly referred as the "dance of the enchantress." As a historically female-dominated genre, men have experienced barriers to learning and performing Mohiniyattam due to gender-based norms and stigmas (that call into question their sexuality and label them as effeminate). But, since the 1980s there has been rising interest among men to learn and embody Mohiniyattam, although social and institutional agencies have continued to negate their ability to access the genre. This dissertation maps male dancers movements between spaces (gendered, artistic, and geographical) as they are taking up Mohiniyattam—upon moving out of the orthodox Indian society into the Indian diaspora of Toronto—and establishing cultural exchange and the transmission of their unique perspectives. This ontological study draws on ethnographic research methods utilized during fieldwork conducted in Kolkata, India, and Toronto, Canada. With three primary case studies and archival and Internet research, I explore the experiences and challenges of male Mohiniyattam dancers who are negotiating hetero-centric biases, gendered norms and stigmas within their socio-political contexts and the dominant cultural ideology, and I consider the broader impacts of these gender-specific limitations and the audience gaze on the embodiment of Lsya or Tandav (movement qualities) in the practice of Mohiniyattam.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 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".