Unveiling Implicit Stereotypes: The Praxis of Taming Men in Indian Households in Yadav’s The Anger of Saintly Men
Bibliographic record
Abstract
Gender disparity is a scourge that engulfs the life of the vulnerable and privileges the dominant one. We are living in the 21st century with modernized thoughts of accepting and normalizing certain stereotypes that were historically marginalized. However, gender stereotypes prevail in the modernist vision. In the wake of sociocultural evolution, we welcome newly socially constructed behavior with a dynamic understanding of flipping gender roles by encouraging words such as “Strong men/women never cry”, and “I have never seen your father cry even at your grandfather’s funeral” prompt to stigmatize vulnerable men in the social spectrum. This continued notion of shutting out vulnerability has become a benchmark for achieving a high standard in society. Nevertheless, male stereotypes in today’s context superseded from being ‘aggressive’ to ‘benevolent’ which again opens a curtain for overshadowing men to act by the social labels. By employing Anubha Yadav’s “The Anger of Saintly Men” (2021), this article aims to represent vulnerable men in Indian society highlighting implicit stereotypes fed by patriarchy to men folks. Using social role theory and qualitative research methodology, this research unfolds the complexities of men’s role in Indian society. The article brings a spotlight on implicit stereotypes among men in Indian society. The findings of the research propound that voiceless men adhere to stipulated masculine norms to fit in the ambivalent society.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".