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Record W2950039534 · doi:10.29333/ejecs/139

Confronting Ambiguity: Reading the Intersection of Racial and Sexual Marginalization in Rex vs Singh and Seeking Single White Male

2019· article· en· W2950039534 on OpenAlexaffabout
Yilong Liu

Bibliographic record

VenueJournal of Ethnic and Cultural Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicFeminism, Gender, and Sexuality Studies
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsAmbivalenceWhite (mutation)ImmigrationRacismQueerAmbiguityGender studiesSociologyIdentification (biology)State (computer science)Intersection (aeronautics)Social psychologyCriminologyPsychologyPolitical scienceLawGeography

Abstract

fetched live from OpenAlex

This paper examines how Canadian filmmakers and artists explore racial and sexual marginalisation in Canada. Two films in particular exemplify different forms of racism towards South Asian immigrants. The first, Rex vs Singh (2008), an experimental documentary produced by John Greyson, Richard Fung, and Ali Kazimi, showcases the ambiguous application of immigration policies to repress South Asian immigration. Through different reconstructed montages, the film confronts these ambiguities in relation to the court case. The second, Seeking Single White Man (2010), a performance-video work by Toronto-based artist Vivek Shraya—South Asian descent, demonstrates not only the dominant racial norms and white normativity in the queer community in Toronto, but also the ambivalence in the performance and in racial identification. I identify ambiguity as the distinct contribution to understanding first: i) how state policies are used for racial and sexual repression, ii) the ways in which identification of racial norms are unstable, iii) and how these norms have been translated into sexual (un)/desirability. The ambiguities evoked by these works provide critical insights to investigate the complexity of racial marginalisation and their intersection with gender/sex normativity.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.256

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 head0.106
GPT teacher head0.378
Teacher spread0.271 · 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
Published2019
Admission routes2
Has abstractyes

Explore more

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