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Record W4386560777 · doi:10.1177/13678779231197696

Streaming and India's film-centred video culture: Linguistic and formal diversity

2023· article· en· W4386560777 on OpenAlexaff
Ishita Tiwary

Bibliographic record

VenueInternational Journal of Cultural Studies · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsConcordia University
Fundersnot available
KeywordsTimelineMovie theaterDiversity (politics)The InternetMedia studiesCreative industriesSociologyAdvertisingPolitical scienceBusinessHistoryVisual artsWorld Wide WebComputer scienceArtLaw

Abstract

fetched live from OpenAlex

In this article, I foreground the importance of the ‘cinematic’ as the most important vector of video cultures in India. The article identifies how the timeline of video culture disruption in India deviates from countries with stronger television-based cultures. The availability of videocassettes and their ability to make movies more widely available was consequently of greater consequence in India than in other places, and a development that was still adjusting the video culture as digital distribution arrived. Internet distribution and digital production technologies have also brought significant changes to India's viewing culture, though again, the peculiarities of the Indian market make these changes distinctive. Where many countries have encountered greater access to foreign-produced content and services, key digital changes in India tie into access to and interest in a broader range of domestic cinema. The following analysis flags key moments of disruption and explores discussion of the emergence of pan-Indian film that coincided with streaming platform adoption in 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.006
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0000.001
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 head0.048
GPT teacher head0.280
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations12
Published2023
Admission routes1
Has abstractyes

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