Funny Boy: Dismantling the System of Carnal and Racial Autocracy
Bibliographic record
Abstract
The horrific ethnic clashes between the minority Tamils and the majority Sinhalese in Sri Lanka during the early 1980s are the backdrop for Shyam Selvadurai’s novel, Funny Boy (1994). Selvadurai is a Sri Lankan-Canadian writer. Six chronologically related stories that center on the subaltern core character's gender, sexual orientation, and race make up the novel. Arjun Chelvaratnam, also known as Arjie, is the main character. He comes from a Tamil minority household and is subjected to strict and oppressive rules from his patriarchal family, which prevents him from engaging in his passion of cross-dressing. These rules are contrasted with a string of tragic ethnic conflicts that occur throughout the nation. Because of his non-traditional sexual orientation, Arjie feels sexually uncomfortable in his own family and faces political limits due to his race. This research seeks to investigate Arjie's battle to liberate himself from the constraints of gender and desirability and accept his emerging sexuality. Michel Foucault's concept of power is also referred to in order to obtain theoretical insight into the process of gendered "othering" and to provide critical opinions on the marginalization of the third gender as a power discourse in 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.038 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".