Bibliographic record
Abstract
This essay argues that Arundhati Roy’s inclusion of numerous Indian vernacular words and phrases in her fiction is carefully calibrated to serve the author’s activist political agenda. This is true not only of her first novel, The God of Small Things, but also of the more recent Ministry of Utmost Happiness. Both feature a Bakhtinian or dialogic interplay of linguistic modes. The earlier work poses two languages against each other: Malayalam, the primary language of Kerala, and English, the medium of narration and the preferred tongue of the prominent Ipe family. The outcome of this contest highlights the Ipes’ imprisonment within a life-denying straitjacket of outworn prejudices and conventions. In The Ministry of Utmost Happiness the linguistic terrain broadens to include several tongues of the subcontinent, along with English. Roy gives special exposure to two: Urdu and Kashmiri, to reclaim them from the oppression both of them, along with their speakers, are undergoing at the hands of the dominant Hindi-speaking majority. Tilo, a pivotal character, is enthusiastically polyglot, a trait which accords with her more general adaptability and freedom from sectarian narrowness. The other central figure, the transgender Anjum, resembles Tilo in her resistance to strict definitions of her fluid selfhood, but must endure forms of verbal as well as physical violence. Like her first novel, but on a more capacious stage, Roy’s second aims at speaking multilingual truth to monolingual power.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".