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Record W2065104075 · doi:10.1080/09663690500094922

Because Pigs Can Fly: Sexuality, race and the geographies of difference in Shyam Selvadurai's<i>Funny Boy</i>

2005· article· en· W2065104075 on OpenAlexaboutno aff
Tariq Jazeel

Bibliographic record

VenueGender Place & Culture · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
FundersEconomic and Social Research CouncilRoyal Geographical Society
KeywordsTamilGender studiesSociologyPoliticsUnrestNarrativeRace (biology)Political scienceArtLawLiterature

Abstract

fetched live from OpenAlex

This article explores the ways that the popular diasporic novel Funny Boy, set in Sri Lanka but written from Canada by an exiled Sri Lankan born Tamil, intervenes in the country's contemporary geographies of difference. The novel itself explores a Tamil boy's struggle to negotiate life in Sinhala-dominated Colombo while also coming to terms with his emergent same-sex desire. By focusing specifically on the writing of two familiar middle-class Sri Lankan spaces central to the novel's narrative—the family home and the school—the article shows how these everyday geographies regulate and normalise carnal desire in a society which still operates anti-homosexual legislation. It also suggests how the erosion of the meanings of these familiar spaces is a tactic central to the main protagonist's sexual liberation. By reading these sexualised geographies through the polemic racialised Sinhala/Tamil divisions in contemporary Sri Lankan society, the paper shows how the novel makes an important political intervention in contemporary Sri Lankan politics where devolution and federal solutions to recent civil unrest have produced territorialised geographies of difference that prescribe 'places for races'. By evoking the Funny Boy's fictive and sexualised geographies of exclusion and resistance, this article unsettles the logic that binds intra-racial solidarity, its cognate geographical modelling, and instead highlights the exclusions that exist at all levels in Sri Lankan society.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.278
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2005
Admission routes1
Has abstractyes

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