The transnational tales of an Indian creative producer: the case of Guneet Monga
Bibliographic record
Abstract
Studies of Indian cinema have traditionally placed more emphasis on directors, stars, aesthetics and issues of ideology than on the practices of creative producers. Although these are important concerns, the role of Indian producers deserves careful scholarly attention, especially in the context of transnational film projects, where producers are often involved from the pre-development stage through production and distribution. To address this problem, this article examines the production stories of an Indian creative producer, Guneet Monga (1983-), who is well-known for setting up Indian-European co-productions that deviate from contemporaneous spectacle-driven mainstream Bollywood productions. Through an in-depth personal interview with the producer herself and insights from film and media production studies, this article demonstrates how Monga’s micro-production stories reveal larger creative and collaborative practices that are transforming India’s independent film production culture and making it more transnational. This article shows that producers––the least researched figure in Indian film scholarship––gain several navigational tactics through transnational co-productions such as telling tales of tenacity, hustling and interpersonal networking, among others. These tactics, in turn, challenge the precarious conditions of working in the Bollywood-dominated film culture of India.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".