Bibliographic record
Abstract
This article is connected to my project concerning the Indian film industry, questions of work and women’s labour; and, this interview-based article grows from my ongoing audio-visual documentary work tentatively titled The Shadow and The Arc Light. It enables me to reflect upon my previous book project, and takes shape in the light of contemporary feminist historiography. Suhasini Mulay began her career as an actor with Bhuvan Shome (Mrinal Sen 1969), and thereafter, shifted to filmmaking and obtained technical training from McGill University, Montreal. Upon her homecoming she initially worked with the International Film Festival of India, and later assisted Satyajit Ray (for Jana Aranya [1975]) and Mrinal Sen (for Mrigayaa [1976]). While Mulay has made documentaries, and also acted in Bhavni Bhavai (Ketan Mehta 1980), she eventually resumed full-time acting in 1999. As narrated by her, she endured hierarchical, gendered and precarious work conditions, which inform us about the arduous production milieu. Through such conversations, I propose gender as a lens for studying film history, and underscore filmmaking as labour; as well, analyse the nature of filmic work. I aspire to demonstrate how women involved in various capacities, negotiate networks of media forms and capital, and persist perilously.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".