Queering the Colonial in Shyam Selvadurai's Swimming in the Monsoon Sea
Bibliographic record
Abstract
In my paper “Queering the Colonial” I will analyze Sri-Lankan-Canadian novelist Shyam Selvadurai’s novel Swimming in the Monsoon Sea (2005) to facilitate a discussion where a diasporic heterosexual intervention enables South Asian queer discursive spaces initiate new alliances with heteronormativity that blur the boundaries between homosexuality/heterosexuality. Further, my study will help reflect on how a return to pre-colonial South Asian architecture – an amalgamation of styles from Greece, Afghanistan, and India – can expand the idea of a global South Asia across time and space. In my article I will show that the colonial silencing of a two-thousand-year-old civilization was never complete; it is only through a strategic negotiation with a pre-colonial past that postcolonial South Asia can initiate non-reductive non-western epistemologies of understanding South Asian queer and female. While examining how South Asian homosociality complicates western attitudes toward queerness, I will also extend the conversation to show a shift in power when teenage Amrith de-centers Othello and defies traditional portrayal of women, for instance, of a passive Desdemona. Drawing on South Asian feminist, queer, and postcolonial theories, the paper will negotiate across different borders, critique extant colonial ideologies, and open up new interpretive frameworks of understanding postcolonial South Asian literature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| 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".