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‘You are Nathan F*cking Shelley!’

2025· book-chapter· en· W4410045173 on OpenAlexaff
Adam Ehsan Ali, Matt Ventresca

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsWestern University
Fundersnot available
KeywordsArtArt historyPhilosophy

Abstract

fetched live from OpenAlex

Abstract This paper analyses the representation of Nathan Shelley, a central racialized character in the Emmy-award winning television series Ted Lasso who becomes an assistant coach of AFC Richmond after being discovered by the show’s protagonist, Ted, but later betrays his mentor. It argues that Ted Lasso reproduces stereotypically gendered understandings of Muslim identity by portraying Nate’s character through a form of ambivalent masculinity and inept heterosexuality that reinforces Orientalist conceptions of Muslim—and ‘Muslim-looking’—men. These portrayals of Nate, and his eventual villainous turn, mirrors racialized understandings of Muslim radicalization in the post-9/11 era involving the construction of Muslim-looking subjects (but mostly men) as inherently suspicious. As such, Nate’s ambiguous Brownness and the show’s colour-blind writing of his character’s backstory combine to construct him as a vulnerable but risky subject whose proper development is dependent on Ted Lasso’s white protagonists. The show’s racial boundaries produced in Nate’s character arc, which reproduce colonial logics, draw civilizational differences between Westerners and development subjects, the latter of whom require intervention and modernization within the Western world. At the same time, this character arc amplifies Western anxieties about the ‘inherent’ riskiness of Brown, Muslim-looking men in the post-9/11 era. As such, this analysis of Nate unsettles Ted Lasso and casts a shadow over its progressive, ‘feel-good’ message of hope and optimism by demonstrating how the show promotes a racialized construction of Nate as an emasculated, dangerous, and ahistorical subject. Citation: Ali, Adam Ehsan and Matt Ventresca, ‘“You are Nathan F*cking Shelley!”: Orientalism, White Saviourism, and the Radicalization of Nate in Ted Lasso’ (20 Mar. 2025), in Shakuntala Banaji and Janelle Joseph (eds), Media, Entertainment, and Sport, in Meena Dhanda (ed.), Oxford Intersections: Racism by Context (Oxford, online edn., Oxford Academic, 20 Mar. 2025 -), https://doi.org/10.1093/9780198945246.003.0067, accessed [date].

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: Other · Consensus signal: Other
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.004
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.002

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 head0.040
GPT teacher head0.294
Teacher spread0.254 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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