Queer Politics of Naming and Figuration of the “Lesbian” in <i>Maja Ma</i>
Bibliographic record
Abstract
In post-377 neoliberal India, with the emergence and proliferation of subscription-based video streaming platforms, the cultural field has undergone a sea change. In this complex, shifting, and evolving cultural terrain, representation of queer/lesbian women is becoming less uncommon. Maja Ma contributes to this growing cultural landscape by offering the representation of a “lesbian” who is also a Hindu mother, aunty, and wife. In this paper, I explore what the politics of naming—which has largely been avoided by films committed to the representation of queer women in Hindi cinema—offers the film’s “lesbian” protagonist, what it reveals about her complex figuration as aunty-mother-wife, and what naming offers relationalities that are forged, informed, and altered by the private disclosure as well as public revelation of her queerness. Drawing on queer cultural studies scholarship focused on India, as well as scholarship on the complexities of “lesbian” activism in India, I interrogate the shift that Maja Ma engenders in Indian queer cultural landscape, and the queer possibilities it offers this field, while paying attention to dominant norms of representation it aligns itself with.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".