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Record W4395961737 · doi:10.1080/19406940.2024.2342394

‘Fog on the tyne’? The ‘common-sense’ focus on ‘sportswashing’ and the 2021 takeover of Newcastle United

2024· article· en· W4395961737 on OpenAlexfundno aff
Stephen Crossley, Adam Woolf

Bibliographic record

VenueInternational Journal of Sport Policy and Politics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
FundersLaidlaw Foundation
KeywordsNewcastle upon tyneFocus (optics)Coronavirus disease 2019 (COVID-19)Political scienceHistoryEconomic historyMedicine

Abstract

fetched live from OpenAlex

On 7 October 2021, a controversial takeover of the English Premier League team Newcastle United Football Club saw an 80% stake acquired by the Saudi Arabian Public Investment Fund (PIF), the country’s sovereign wealth fund. Public discussion and media coverage of the takeover has revolved almost entirely around the concept of ‘sportswashing’ – the practice of (usually) undemocratic regimes using sporting investments to ‘cleanse’ or enhance their reputation and deflect attention away from human rights abuses. This article examines the Newcastle takeover, interrogating the widespread portrayal of it as a clear-cut case of sportswashing, and explores alternative explanations for the purchase, and potentially other sports-related investments. Drawing broadly on scholarship by Bourdieu and scholars of the Arabian Peninsula, it argues that the concept of sportswashing as it is currently used limits discussion of wider, more complex social, political and economic entanglements.

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)0.004
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 score0.108
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.033
Scholarly communication0.0100.007
Open science0.0010.006
Research integrity0.0030.004
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 head0.039
GPT teacher head0.359
Teacher spread0.321 · 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

Citations27
Published2024
Admission routes1
Has abstractyes

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