Humour in Indian writing in English: three novels women writers : Namita Gokhale's Paro dreams of passion, Suniti Namjoshi's The conversations of cow, Arundhati Roy's The god of small things
Bibliographic record
Abstract
The key words in the title of the thesis are problematic and need to be defined at the outset. Humour, which is ordinarily conflated with comedy, satire, irony and its various other forms, presents problems of terminology. The adjectives 'Indian' and 'women' used to qualify the writers, also present some difficulties. Before launching into a discussion of humour and the artistic modes and genres one associates with it, it is important to address the question of the credentials of 'Indianness' of the three writers. Gokhale (b. 1956) author of Paro Dreams of Passion, started out as a journalist who still lives and works in India. Roy (b. 1961) the Booker prize-winning author of The God of Small Things, is touted as a 'home-grown' who has neither studied nor lived abroad. Namjoshi (b. 1941) has lived abroad, and taught English literature in Canada and now works in the U.K. However, her themes and inspirations are as 'Indian' as those of Anita Desai, Ruth Prawer Jhabvala or Shashi Deshpande. She belongs within the category of diasporic Indian writers and attributes some of her textual tactics to India. Namjoshi wrote 'The Conversations of Cow' in Canada and is included in books of literary criticism (Naik & Narayan, 2001) as a diasporic writer of Indian origin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".