Construction of Differences Through Movies: A Case Study of Portrayal of Kashmiri Muslims in Indian Movies
Bibliographic record
Abstract
Indian movies are very popular in sub-continent and have equal rate of viewership in Pakistan as they have in India. On the other hand, movies have been known as the best tool for agenda setting since years. This had been experimented successfully at first in second world war and afterwards in USSR-Afghan war. This paper explores the portrayal of Kashmir’s in Indian movies in the same context of agenda setting. The main objective of the study is to determine whether Kashmiri Muslims are positively or negatively portrayed in Indian movies and are given equal representation or not. The researcher has employed the survey research and content analysis method for the study. Three Indian movies involving Kashmiri characters have been selected for content analysis. For the survey purpose, students of University of Punjab have been selected as population and a sample size of 150 have been taken through simple random sampling. The results of the study show that Kashmiri Muslims are portrayed as rebels and terrorist, and, are given only negative characters to perform. The study explains this phenomenon with help of Agenda Setting Theory.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".