The athlete–doctor relationship: power, complicity, resistance and accomplices in recycling dominant sporting ideologies
Bibliographic record
Abstract
Sociological investigations into the athlete–medical practitioner relationship are scarce due to medical bias for positivist epistemologies. The aim of this research was to identify the scope and purpose of medical interventions for four athletes, within the context of social processes that enable medicine to claim athletic bodies as objects of practice and performance. The role and function of power in the athlete–medical doctor transaction and athlete embodiment were also of interest. Using a story-analyst approach grounded in narrative analysis, the ideologies of ‘slim to win’ and ‘performance’ were identified as the impetus for the athletes seeking the expertise of doctors. Doctors were positioned as accomplices in ‘slim to win’ and ‘performance’ ideologies within the athletes’ stories, which influenced medical practices and compromised athlete health. Disciplinary power was enacted when the doctors observed, corrected and manipulated the athletes’ bodies through medical practice. Athletes also had agency through renegotiating the meaning of the ‘treatment’ process by reconfiguring medical doctor’s disciplinary power as forms of empowerment knowledge. This research highlights the complex nature of the athlete–medical doctor transaction and how these encounters can be productive and oppressive for athletes.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.013 | 0.059 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".