Cultural Monsters in Indian Cinema: The Politics of Adaptation, Transformation and Disfigurement
Bibliographic record
Abstract
In India, a popular trope is adapting cultural myths and religious iconographies into visceral images of the monster in literary and visual representations. Cinematic representations of the Indian monster are modelled on existing folklore narratives and religious tales where the idea of the monster emerges from cultural imagination and superstitions of the land. Since it rationalizes several underlying archetypes in which gods are worshipped in their monstrous identities and disposition, the trope of the monster is used in cinema to indicate the transformation from an ordinary human figure to a monstrous human Other. This paper examines cinematic adaptations of monster figures in Malayalam cinema, the South Indian film industry of Kerala. The cultural practice of religious rituals that worship monstrous gods is part of the collective imagination of the land of Kerala through which films represent fearsome images of transformed humans. This article argues that cultural monsters are human subjects that take inspiration from mythical monster stories to perform in a terrifying way. Their monstrous disposition is a persona that is both a powerful revelation of repressed desires and a manifestation of the resistance against certain cultural fears associated with them. The analysis of several Malayalam films, such as Kaliyattam (1997) Manichithrathazhu (1993) and Ananthabhadram (2005), reveals how film performance adapts mythological narrative elements to create new cultural intertexts of human monsters that are psychotically nuanced and cinematically excessive.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.027 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".