Streaming and India's film-centred video culture: Linguistic and formal diversity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".