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Record W4285215026 · doi:10.1123/ssj.2021-0172

“Anesthetized Gladiators:” Painkilling and Racial Capitalism in the NFL

2022· article· en· W4285215026 on OpenAlexaff
Matt Ventresca, Samantha King

Bibliographic record

VenueSociology of Sport Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsQueen's University
Fundersnot available
KeywordsLeagueFootballCapitalismNarrativeContext (archaeology)Political scienceSociologyMedia studiesGender studiesLawHistoryLiteratureArt

Abstract

fetched live from OpenAlex

Drawing on an extensive archive of media texts collected between 2014 and 2019, we trace shifting representations of the National Football League in discourse on painkiller use among its players. We argue that in contrast to earlier eras, an image of the league as an exploitative and corrupt institution has come to the fore. Clustered around the announcement of a series of player lawsuits, these discourses are tempered by the persistence of narratives of personal responsibility and the elision of racial logics that predetermine athletes’ subjection to pain and injury. Situating our analysis in the context of the drug wars and the profit motive of the National Football League, we argue that these discourses both reflect and contribute to the workings of racial capitalism across the professional football and pharmaceutical industries.

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)0.005
metaresearch head score (Gemma)0.008
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 score0.030
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0120.022
Scholarly communication0.0080.007
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.293
Teacher spread0.271 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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