A “Messy” History and its Many “Messy” Texts: An Essay on Partition (India, 1947) and its Narratives
Bibliographic record
Abstract
Abstract The division of British India into India and Pakistan in August 1947 was accompanied by the dislocation of between twelve and sixteen million people and the violent deaths of around a million. Punjab and Bengal, the two provinces that were divided, were the most affected but so were other parts of the country. After all, mixed populations (Muslim, Hindu, Sikh, Christian, and so on) were more the norm than not in rural and urban India, making the very notion of two homelands, one with a Muslim majority and another with a Hindu majority, somewhat difficult to realize. Apparently the leadership expected what was euphemistically referred to as “an orderly exchange of population” in spite of the fact that the boundaries were officially announced on August 17, 1947, that is, after the actual transfer of power to the two successor states on August 14–15, 1947. Individuals, families, and communities that found themselves on the “wrong” side of the border were dispossessed of land and home, faced with the threat of bodily harm, spent months on the road and in refugee camps, and began the long process of resettlement. The place where the shock and disbelief first register, as do attempts to negotiate an impossible history of violence, is the literary text. This article attempts to introduce a literature that self‐identifies with this traumatic historical experience. Partition literature is best contextualized by developments in two academic, disciplinary fields: history and literary criticism. Disciplinary history has only recently acknowledged the need for a social history of Partition and literary criticism has only recently expanded to allow for ways of discussing a traumatic literature other than the limiting one of “literariness.” Thus the article attempts to interweave a discussion of Partition literature with a discussion of shifting critical approaches to it. By beginning with a look at Partition's erasure in disciplinary history, the article aims to encourage the readership to consider ways in which the historiographical has informed or shaped Partition literature and the history of its reception.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.045 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".