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Record W2165251744 · doi:10.1123/ssj.2012-0185

When is a Drug Not a Drug? Troubling Silences and Unsettling Painkillers in the National Football League

2014· article· en· W2165251744 on OpenAlexaff
Samantha King, R. Scott Carey, Naila Jinnah, Rob Millington, Andrea Phillipson, Carolyn Prouse, Matt Ventresca

Bibliographic record

VenueSociology of Sport Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsQueen's University
Fundersnot available
KeywordsSilenceFootballLeagueNarrativeSociologyGender studiesAmerican footballAthletesSanctionsMedia studiesCriminologyAestheticsPolitical scienceLawMedicineLiteratureArt

Abstract

fetched live from OpenAlex

This paper uses a genealogical approach to explore the conjuncture at which the longstanding but partial and uneasy silence surrounding painkiller use in the National Football League seems increasingly under threat. We historicize and problematize apparently self-evident narratives about painkiller use in contemporary football by interrogating the gendered, racialized and labor-related discourses surrounding Brett Favre’s 1996 admission of a dependency on Vicodin, as well as the latest rash of confessions of misuse by now retired athletes. We argue that these coconstructed and coconstructing moments of noise and silence are part of the same discursive system. This system serves to structure the emerging preoccupation with painkillers in the NFL, with Favre’s admission still working to placate anxieties surrounding the broader drug problems endemic to the league, and failing to disrupt our implicit knowingness about painkiller use, thus reinforcing ongoing cultures of silence and toughness in professional football.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.181
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.311
Teacher spread0.270 · 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 teacher head, 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

Citations24
Published2014
Admission routes1
Has abstractyes

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