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Record W3197988842 · doi:10.1353/ari.2021.0029

"A Different Economy": Postcolonial Clearings in David Chariandy's Brother

2021· article· en· W3197988842 on OpenAlexaboutno aff
Gugu Hlongwane

Bibliographic record

VenueAriel · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsBrotherPremiseIdentity (music)SociologyRace (biology)RacismState (computer science)Gender studiesLawGenealogyHistoryPolitical scienceArtAestheticsPhilosophy

Abstract

fetched live from OpenAlex

This article explores the myriad of ways in which racial identity and geographical location are deterministic factors in David Chariandy's Brother (2017). Borrowing from theories of critical race scholars, including Rinaldo Walcott, Idil Abdillahi, and Frantz Fanon, this article argues that Chariandy's book is an exemplar of how an economy based on intrinsic value privileges human bonds over money. In response to dominant Canadian discourses that position Black men as criminals, Chariandy's novel celebrates Black masculinities and reveals how law enforcement haunts the communities, homes, and small businesses of Black people. The characters in Brother find refuge in what I call postcolonial clearings, which take the form of barbershops, hidden valleys, and music. This article begins with the premise that Canada is a colonized territory that treats Black people as second-class citizens. The article underscores police brutality which in Brother—a text set in the Toronto of the mid-1990s—is directed at racialized people, especially Black men. Chariandy not only breathes life into Black men rendered nameless and faceless by powers-that-be, but he also questions the central ideals and pillars of the Canadian nation-state.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.423

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0490.025
Scholarly communication0.0080.003
Open science0.0010.005
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0060.000

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2021
Admission routes1
Has abstractyes

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