“The Only Thing I Am Guilty of Is Taking Too Many Jump Shots1”: A Deleuzian Media Analysis of Diana Taurasi’s Drug Charge in 2010
Bibliographic record
Abstract
In November 2010, the US media reported that basketball player Diana Taurasi tested positive for a banned substance while playing in Turkey. In this study, we explore the media coverage of Taurasi’s positive drug test from a Deleuzian perspective. We consider the media coverage as an assemblage (Deleuze & Guattari, 1987; Malins, 2004) to analyze how Taurasi’s drug using body is articulated with the elite female sporting body in the coverage of her doping incident (Markula, 2004; Wise, 2011). Our analysis demonstrates that Taurasi’s position as a professional basketball player in the US dominated the discussion to legitimize her exoneration of banned substance use. In addition, Turkey, its “amateur” sport and poor drug control procedure, was located to the periphery to normalize a certain type of professionalism, doping control, and body as the desirable elements of sporting practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".