Bibliographic record
Abstract
Representation of contemporary politics and human problems is a major theme for contemporary litterateurs and social thinkers. A number of prolific and eminent novelists such as Rohinton Mistry, Salman Rushdie, Vikram Seth, Khushwant Singh, Nayantara Sahgal, Shashi Tharoor, Amitav Ghosh, Arundhti Roy, Kiran Desai and many more have tried to explore the hidden truth and treacherous activities carried out over Indian citizens by their elected political representative on the name of different government schemes. Rohinton Mistry, an Indian of Parsi in origin presently living in Canada, represented contemporary Indian politics in his novels by subverting the conscious or unconscious cultural categorisations associated with the forms of novels focussing on the human condition located in time and space. He tried to show us how politics is used by politicians of all parties to remain in power for fulfilling their vested interest. Present paper is an effort to describe and discuss how political upheavals have an impact on the psyche of common man. It would discuss the theme of politics in the fiction of Rohinton Mistry.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| 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".