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Record W1594477585

Men Negotiating Identity in Zadie Smith's White Teeth

2009· article· en· W1594477585 on OpenAlexvenueno aff
Taryn Beukema

Bibliographic record

VenuePostcolonial text · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
Fundersnot available
KeywordsMasculinityWhite (mutation)Identity (music)NarrativeNegotiationGender studiesNationalityColonialismIdentity negotiationSociologyAestheticsHistoryArtLiteratureImmigrationSocial scienceArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Zadie Smith's novel White Teeth examines the masculine experience (both migrant and English) by reflecting on the complex effects that the history of Britain's colonial enterprise can have on one's identity. While her text mirrors many of the modern narrative responses to living in post-imperial British society and adapting to its multiplicities of identity, Smith redeploys the traditional conventions of the contemporary British novel by shifting between generational analyses of masculinity and focusing on the changing social codes between the past and the present. Smith challenges social constructions of masculinity by dissecting cultural belonging and nationality, analyzing the ways in which masculinity is ruptured and distorted (both in behaviour and in practice) in the various narratives of identity. Most importantly, the novel maps the significance of a person's roots/routes, necessitating an exploration of the history and journey involved in negotiating a masculine identity in the new postcolonial world.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.018
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.020
Scholarly communication0.0080.004
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.021
GPT teacher head0.337
Teacher spread0.316 · 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

Citations7
Published2009
Admission routes1
Has abstractyes

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