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Record W4390112189 · doi:10.5430/wjel.v14n1p527

Rampage of Institutional Racism in Zadie Smith’s On Beauty

2023· article· en· W4390112189 on OpenAlexvenueno aff
J. Jenifer, Ajanta Sircar

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLiterary Theory and Cultural Hermeneutics
Canadian institutionsnot available
Fundersnot available
KeywordsRacismSociologyCriminologyContext (archaeology)BeautyIdentity (music)Institutional racismGender studiesRace (biology)Critical race theoryLawPolitical scienceAestheticsHistoryArt

Abstract

fetched live from OpenAlex

Racism can be observed in physical or verbal harassment directed at someone because they are of a certain racial or cultural origin. For instance, people, including children, may be subjected to discrimination at work or in schools based on race. Institutional racism is also pervasive in areas such as criminal justice, housing, healthcare, and employment, yet many organizations are unaware of how their rules and practices harm some people. To illustrate how Zadie Smith places race in the foreground of her intersections of American, British, and Commonwealth identity, examination of the setting, Cambridge (called Wellington) and Boston is crucial. In this context, this research paper aims to analyze Zadie Smith’s novel On Beauty (2005) in the light Kimberle Cerenshaw’s of Critical Race Theory (CRT). This scholarly investigation also probes the myriad forms of institutional racism including racial politics, color-blindness and Afro- pessimism within a British University setting.

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: none
Teacher disagreement score0.016
Threshold uncertainty score0.038

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 studies0.0130.022
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.242
Teacher spread0.223 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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