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Record W4290630214 · doi:10.1080/25785273.2022.2109846

The transnational tales of an Indian creative producer: the case of Guneet Monga

2022· article· en· W4290630214 on OpenAlexaff
Neha Bhatia

Bibliographic record

VenueTransnational Screens · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsScholarshipMovie theaterContext (archaeology)MainstreamProduction (economics)SociologySpectacleMedia studiesIdeologyAestheticsGender studiesPolitical scienceVisual artsHistoryPoliticsArtLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.021
GPT teacher head0.236
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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