Reading Across the Curriculum: Using the Fiction of the Indian Subcontinent in Social Science Classes
Bibliographic record
Abstract
T here is a publishing boom in fiction by authors from the Indian subcontinent.Indian, Pakistani, Sri Lankan, and Bangladeshi authors are being discovered almost daily.The literature from India is several thousand years old.However, following the notoriety of Salman Rushdie, the meteoric success of Arundhati Roy's novel The God of Small Things, and the Oscar-winning screen adaptation of Michael Ondaatje's The English Patient, it is almost impossible to open the New York Times Book Review without reading of another new highly-praised novelist from the region.Most of these authors write in English.Many are expatriates, living in Canada, England or the United States.These novels, with the variety of experiences described, in settings that are exotic and often unknown to the average high school or college student, are ideal for a Reading Across the Curriculum assignment in the social sciences.In the assignments, each student reads a work of fiction from an approved bibliography and writes a book review that applies the sociological, political, historical, and/or economic concepts that have been covered in class to the contents of the novel.Often the student is also required to research the cultural, ethnic, or national milieu in which the novel is written. Special Section on Teaching Indic Traditions
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".