Fragile fat masculinities: the narrative construction and masculine negotiation of fatphobia in Malayalam cinema
Bibliographic record
Abstract
The portrayal of the male fat body in Indian cinema is intertwined with prevalent social constructs and gender stereotypes regarding idealized masculine corporeality. The fat character is frequently depicted as a source of humour that not only reinforces fatphobic attitudes but also underscores the normalization of body shaming in Indian society. This article scrutinizes the representations and discourses surrounding fatness within the context of Malayalam cinema, the South Indian film industry based in the state of Kerala. It specifically analyzes two key Malayalam films to understand how visual narratives construct fat bodies using humour to shed light on the protagonist’s struggles with body image, societal prejudices, and self-acceptance. This paper argues that the construction of fatphobia degrades male characters and reduces them to objects of revulsion, thereby reinforcing stereotypes of desirability and beauty. These films use a common narrative that presents fat characters as kind-hearted, childish and feminized to create a humanized body image to conceal the fatphobia that surrounds their masculinity. These forced narrative negotiations reinforce fragile fat masculinities through the exaggerated social performance of fatness as something that invites a pleasurable gaze without destigmatizing the insecurities and societal norms around body image and masculinity.
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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 | 0.000 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".