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Record W2525584172

Funny Boy and the Pleasure of Breaking Rules: Bending Genre and Gender in "The Best School of All"

2015· article· en· W2525584172 on OpenAlexvenueno aff
Kaustav Bakshi

Bibliographic record

VenuePostcolonial text · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsnot available
Fundersnot available
KeywordsQueerPleasureDialogicSociologyAppropriationGender studiesDiasporaNarrativeNationalismIdentity (music)PoliticsAestheticsLawPolitical scienceLiteraturePsychologyArtLinguistics
DOInot available

Abstract

fetched live from OpenAlex

This paper focusing on the fifth of the six stories, “The Best School of All,” that constitute Funny Boy , explores sexual identity politics on the school campus, by analysing how Selvadurai’s appropriation of the generic English school story, invests the popular genre with a disruptive potential, unknown to its original form, thereby generating immense pleasure in the reader. The paper argues that the pleasure in reading “The Best School” independent of the novel is to discover in it the familiarity of the school story, and also how it is constantly de-familiarised. By relating theories of genre, gender, nation and the diaspora, the paper attempts to show that “The Best School,” not only subverts generic rules by transfiguring an overtly masculinist genre to accommodate queer desires; it also opens up a dialogic space by confronting authoritative discourses on the colonial system of values perpetuated through the educational institutions, compulsory heterosexuality and ethno-centric nationalism of the postcolonial nation-state. In effect, “The Best School” becomes an important node in the dialogic network of queer narratives produced across the globe, qualifying as a “cause” novel: it advocates recognition of non-heteronormative identities and desires in a postcolonial nation, which is politically and morally opposed to legalising homosexuality, still criminalised under a Draconian colonial law.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.089
GPT teacher head0.351
Teacher spread0.262 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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