Anindya Raychaudhuri. <i>Narrating South Asian Partition: Oral History, Literature, Cinema</i>.
Bibliographic record
Abstract
Part way through Narrating South Asian Partition: Oral History, Literature, Cinema, in a chapter which mulls the particular twist of meaning the 1947 Partition of India forced onto “the train,” Anindya Raychaudhuri, remarks: “Partition here, as in so many places in the subcontinent, is both ancient history and contemporary truth” (114). This reflection resonates all the way through the chapters of a book that intuits liminality, marking the slow absorption of the 1947 Partition into the worlds of private remembrance and public representation, from family stories told and not told, to the cultural spaces of literature and film. Since the 1980s, scholars of colonial and modern South Asian history and culture have struggled to locate vocabularies commensurate with the truth of the generations that have, directly or indirectly, lived in the shadow of the 1947 Partition. They have turned to literary and cultural witnessing of this history of rupture, which is frequently juxtaposed with or placed alongside oral testimonies, memoirs, and the many other forms remembrance has taken. Narrating South Asian Partition, rich with analytic detail, well-researched, and inventive, is a welcome addition to this decades-long social history project-in-the-making that now, importantly, includes brick-and-mortar as well as virtual museums and archives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".