“I am different”: Navigating Queer Identity in 1980s Sri Lanka
Bibliographic record
Abstract
This paper studies the portrayal of queerness in Shyam Selvadurai’s novels, Funny Boy and Swimming in the Monsoon Sea, focusing on the theme of homosexuality and character development. Further, the study delves into the significance of mother figures and female relationships in their lives, as well as their love for literature and art, which serves as a platform for introspection and self-expression. Drawing upon the method of textual analysis, the research examines the external and internal confinement experienced by the protagonists and their emotional journeys as they grapple with their identities. It explores how societal norms, family expectations, and internal struggles contribute to their need to hide their true selves. The paper also investigates the characters’ evolution from childhood to maturity, as they learn to accept and embrace their sexual orientation. Additionally, the research addresses the novels’ broader context, considering the historical and cultural setting of Sri Lanka in the 1980s. It examines the societal and familial pressures faced by closeted individuals during that time, shedding light on the challenges and emotional turmoil experienced by LGBTQ+ individuals. The analysis further reflects on the impact of the novels on readers and the significance of LGBTQ+ representation in literature. It underscores the importance of empathy, understanding, and acceptance in nurturing an inclusive and diverse literary landscape. The research contributes to a deeper comprehension of the complexities of queerness and self-acceptance in a conservative society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".