Long read review: crisis in sports governance: exploring anti-doping policy and other battlegrounds (part two)
Bibliographic record
Abstract
The world of sport is facing a serious crisis, deepened by a spate of recent controversies such as match-fixing, doping and the abuse of positions in sport organisations for illicit personal gain. Just in the last couple of months we have seen major scandals, including the arrest of high-ranking FIFA officials, the revelation of a state-sponsored doping scheme of Russian athletes and the discovery of unethical practices by sports professionals (including an apparently rigged judging system in boxing at the Rio Olympics). These pose serious challenges to the governance of sport, domestically and internationally. In addition to public debate about an overhaul of the regulation and governance of sport, scholars have had their say. This two-part review focuses on two books written on the subjects of integrity in sports and anti-doping policies: following his preceding review of The Edge, here Slobodan Tomic reviews Anti-Doping: Policy and Governance, edited by Barrie Houlihan and Mike McNamee, as part of the scholarship contributing to the conversation on ongoing and future challenges to the governance of sport.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".