Thinking “Bioethically”: Moving Beyond Critique at the Intersection of Biomedicine and the Sociology of Sport
Bibliographic record
Abstract
In the sociology of sport, there is a consensus that it is not just our job to analyze our sporting worlds but that we must also intervene in them. In this article, we make a case for how bioethics can serve as a tool in the toolkit that can strengthen one’s analysis and commentary at the intersection of the sociology of sport and biomedicine. First, we provide a brief overview of some of the many key literatures in the field that have engaged with biomedicine. Second, we review several of the main lines of criticism of sport and exercise medicine and of physical activity promotion, noting the important insights that have been generated through these critiques. Building on our discussion of critique, we then describe what we see as four “concerning critical tendencies” related to biomedicine that have emerged in our field and their deeper implications. Third, as a way to dampen the impulse to engage in concerning critical tendencies, we lay out our vision for thinking “bioethically.” Together, we argue that thinking bioethically provides an approach to scholarship that centers sociocultural context alongside empirical evidence, with a goal of bringing about actionable and pragmatic solutions to the problems that some in the field have long identified and critiqued.
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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 | 0.124 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.021 | 0.212 |
| Scholarly communication | 0.032 | 0.036 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.027 | 0.042 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".