Bibliographic record
Abstract
The literature on political humour in India has largely evaded the question of how humour intersects with caste stratification. Not much has been written about humour’s potential to discriminate against certain caste groups of the lower social order. Similarly, the traditional media in India has been silent about the issue of caste following which, social media has emerged as the ‘counter publics’ where caste identity can be collectively and freely expressed. Taking the now flourishing brand of English stand-up comedy on the Internet in India as an entry point, this study investigates if the symbolic articulation of caste identities is at all made possible in this genre. Using a combination of discourse analysis and social media analysis, to examine the jokes produced in stand-up shows, this analysis tries to gauge how frequently, and in what ways, caste finds mention in these performances on the Internet. This paper finds that caste identity, and the associated discrimination, are hardly evoked in the comedians’ discourse. And when spoken about, they are often done so in a disparaging light. I conclude this paper by illuminating the ways in which this disparaging humour bolsters caste discrimination, sustains stereotypes and, in the process, conditions the normalized exclusion of lower-caste groupings from the public sphere.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".