Bibliographic record
Abstract
‘Holding this book in your hand, sinking back in your soft arm-chair, your will say to yourself: perhaps it will amuse me and after you have read this story of great misfortunes, you will no doubt dine well, blaming the author for your own insensitivity, accusing him of wild exagger-tragendy is not a fiction all is true’. Honor’s de Balzac, le p’ere Goriot Rohinton Mistry is an important figure in contemporary common wealth s literature and he occupies a significant position among the writers of Indian diaspora. Mistry like Rushdie and many other Indian English writer is an “émigré” who left India in 1970’s to live in Canada. He is the best-known indo-Canadian novelist, his novels namely such a long journey, a fine balance and family matter have been best sellers and received international a wards. Mistry belongs to the burgeoning crop of Indian novelist writing in English to place him rightly among the great Indian English writers in the words of the santwana haldar.“A glowing star in the galaxy that contains luminaries such as vs. Naipaul, Salman Rushdie, Amitav Ghosh, Shashi Tharoor, Vikram Seth and Bharati Mukherjee to mention a few Rohinton Mistry has drawn the attention of the world as an absorbing writer of human experience.” (Santwana, 2006:7)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".