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Record W3019049294 · doi:10.1111/nana.12614

‘Success in Britain comes with an awful lot of small print’: Greg Rusedski and the precarious performance of national identity

2020· article· en· W3019049294 on OpenAlexaffabout
Jack Black, Thomas Fletcher, Robert J. Lake

Bibliographic record

VenueNations and Nationalism · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsDouglas College
Fundersnot available
KeywordsBritishnessHegemonyNationalismNational identityPoliticsSociologyPerformativityNewspaperEmpireColonialismBritish EmpireIdentity (music)Identity politicsMedia studiesGender studiesLawAestheticsPolitical scienceArt

Abstract

fetched live from OpenAlex

Abstract Sport continues to be one of the primary means through which notions of Englishness and Britishness are constructed, contested, and resisted. The legacy of the role of sport in the colonial project of the British Empire, combined with more recent connections between sport and far right fascist/nationalist politics, has made the association between Britishness, Englishness, and ethnic identity(ies) particularly intriguing. In this paper, these intersections are explored through British media coverage of the Canadian‐born, British tennis player, Greg Rusedski. This coverage is examined through the lens of ‘performativity,’ as articulated by Judith Butler. Through a critical application of Butler's ideas, the ways in which the media seek to recognise and normalise certain identities, while problematising and excluding others, can be more fully appreciated. Thus, it was within newspaper framings of Rusedski that hegemonic notions of White Englishness could be performed, maintained, and embedded.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.262
Threshold uncertainty score0.520

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.016
Scholarly communication0.0060.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.034
GPT teacher head0.301
Teacher spread0.267 · 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 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

Citations12
Published2020
Admission routes2
Has abstractyes

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