Drug Policy in Sport: On the Genealogy of the Immoral Status of Anabolic -Androgenic Steroids. A Polemic.
Bibliographic record
Abstract
This research has endeavored to establish that the normalized deviant judgment ascribed to athletes caught using performance-enhancing substances in professional sports organizations is upheld only by virtue of a fallacious assumption; that a breach of the Athletes Professional Code of Conduct is equitable with a breach of both the ethos of our society and of Western professional sports organizations’ “spirit of competition”. The significance of this distortion is recognized in relation to the way professional sports organizations enforce these policies through disciplinary measures that are subjected on docile athletes – effectively measuring and judging athlete’s drug-related choices through appeal to an ideology based on an outdated and pseudo-scientific narrative. It has therefore been my intention to, through a deconstruction of the narrative, distinguish how these athletes have not so objectively disregarded the ethos of the organizations they compete in but rather that they have acted in accordance with the latent ethos of professional sports organization in contemporary Western society and have then been judged, and punished upon their violation of the misguided manifest policies. Working from this premise I have conducted a theoretical analysis of why athletes passively accept their exploitation, and developed policy diagnoses for the organizations in question.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".