IS OTT IN INDIA ‘OVER-THE-TOP’? RE-PRESENTATION, REGULATION AND RELIGIOUS SENTIMENTS IN INDIAN OTT
Bibliographic record
Abstract
The recent OTT regulation measures in India brings Netflix India and Amazon Prime Video, among other subscription based video platforms under the ambit of Ministry of Information and Broadcasting. Analyzing how negotiations of culture interact with the discourse of ‘protecting sensibilities’ in 21st century India, I argue that the discourse of regulation fabricates “representation” and a ‘global’ Indian identity. Manipulating “the terms of appearance”, these images accused of hurting religious sentiments are ‘framed’ to unwittingly reveal themselves , causing a dent in the post-colonial perception of India being a “country where diverse faiths, languages, and cultures co-exist peacefully within the boundaries of a single peacefully within the boundaries of a single state.” Relaying pre-existing panoptic cultural policing that brings India dangerously close to right-wing nationalist propaganda, I examine the role of “religious sentiments” and the dependence on cultural policing in this precarious political climate. I explore what it would mean to submit or to resist this new reality in the seemingly (in)dependent internet age.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".