Sport and second chances? All drug cheats should be banned for life, here’s why.
Bibliographic record
Abstract
The purpose of this critical commentary is to highlight the inconsistencies evident within the discourse of Performance Enhancing Drug (PED) use and Anti-Doping violations. Of most note, the issue related to proper rehabilitation and subsequent reintegration of athletes who have failed drugs tests is reliant on a notion that when athletes return to competition, fairness will prevail. We know that PEDS, in particular steroids and exogenous hormone treatment, confer an advantage even without concurrent training (see Bhasin et.al. 1996). That their effectiveness is not in doubt is consistent with current policy. However, the question of just how advantageous it is for athletes to use them, even just the once, and whether there are any permanent advantages to doing so, is not particularly evident in contemporary discourse. This paper takes the position, using emerging scientific evidence as well as the recollections of UK strength sports administrators, that any consideration of ‘clean’ sport needs to resolve policy with the evidence that permanent advantages accrued from PED use can only be combatted by promoting a ‘natural for life’ standard.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.053 | 0.051 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".