Sexual Violence During Partition in Bapsi Sidhwa’s Cracking India and Deepa Mehta’s Earth
Bibliographic record
Abstract
In this thesis, I will analyze literary and cinematic representations of sexual violence during Partition by comparing Cracking India and Earth. I will discuss both Sidhwa’s and Mehta’s representations of Partition as not just an immediate historical catastrophe but as a lingering cultural presence. However, Sidhwa’s novel focuses on the sociocultural permissibility toward sexual violence that pervaded daily life in Lahore in early 20th century, and which forms the vital backdrop to and explanation for the unthinkable sexual violence that occurred on an extreme scale during Partition. In contrast, Mehta’s film promotes the more widely recognized narrative of Partition, which emphasizes the tragic effects of religious communalism and its attendant nationalism, and which found expression through sexual violence. In what follows, I will suggest that Mehta provides opportunities for catharsis--similar to Butalia’s “remembering in order to forget”--through symbolic reunification amongst South Asia’s religious communities, imagined through film. Indeed, this move from Anglophone novel to Hindi film posits translation not only as a means of reunification but also as an antidote to the nationalist sentiments that ultimately led to the Partition itself. In contrast, Sidhwa’s novel emphasizes the ongoing sexual violence embedded in South Asian cultural norms, which transcends both religious community and national borders, ultimately resisting the trope of Partition as a dramatic break with daily life, and instead representing the continuing undercurrent of violence against women in South Asia. Department: Design Studies Faculty Mentor: Dr. Sara Grewal
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".