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Record W2159489102 · doi:10.1353/ari.2014.0031

Contesting Clarke: Towards A De-Racialized African-Canadian Literature

2014· article· en· W2159489102 on OpenAlexaffabout
Desi Valentine

Bibliographic record

VenueAriel · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsAthabasca University
Fundersnot available
KeywordsMulticulturalismGender studiesLiterary criticismEthnic groupNarrativeCanadian literatureRacial formation theoryDiasporaCriticismRacismSociologyRace (biology)African-American literatureHistoryAnthropologyArtAfrican americanLiterature

Abstract

fetched live from OpenAlex

This article draws on personal narrative, literary criticism, and multicultural Canadian literature to interrogate George Elliott Clarke’s conceptualizations of a Black Canadian literature and a racialized African-Canadian literary canon in his 2002 essay collection Odysseys Home: Mapping African-Canadian Literatures . Clarke’s work is juxtaposed with my own experience as a bi-racial, multi-ethnic, Black, Negro, mulatto, half-caste, African-Canadian woman, and with those of non-Black scholars, to expose the shifting contours of ethnicity and the blurred and blurring boundaries of Canadian blackness in multi-, mixed-, and indeterminately racial Canada. Through these critical comparisons, I suggest that a racialized African-Canadian literary canon excludes the multiple Canadian cultures in which our literatures are formed, and supports racial constructs that no longer fit the shapes of our multi-ethnic, diasporic, postcolonial skins. I conclude that upon the fertile ground tended by Clarke’s Black literary activism, a de-racialized African-Canadian literature may grow.

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)0.005
metaresearch head score (Gemma)0.007
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: none
Teacher disagreement score0.094
Threshold uncertainty score0.679

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0590.047
Scholarly communication0.0200.004
Open science0.0020.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.013
GPT teacher head0.217
Teacher spread0.203 · 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

Citations1
Published2014
Admission routes2
Has abstractyes

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