Protecting or undermining the integrity of sport? The science and politics of the McLaren report
Bibliographic record
Abstract
This study interrogates the ramifications for the integrity of sport of the McLaren Report (2016a McLaren, R., 2016a. The independent person report. Montreal: WADA, Available from: https://www.wada-ama.org/sites/default/files/resources/files/20160718_ip_report_newfinal.pdf [Accessed 3 January 2017]. [Google Scholar]) into doping allegations concerning the Sochi Olympics. Using an evidence-based approach to policymaking, the main claims of the report are subjected to scrutiny. The analysis suggests that McLaren’s report seriously undermines the integrity of Olympic sport for four main reasons:(i) Contrary to the historic neutrality of World Anti-Doping Agency (WADA), it took a political stance by implicating a nation state in wrong-doing without presenting sufficient evidence;(ii) WADA undermined trust in the decision-making process concerning the enforcement of compliance with its Code by using a highly questionable methodology for gathering information;(iii) WADA exposed its own inadequacies by allowing top athletes to use banned substances, and(iv) WADA undermined its main mandate as an enforcer of compliance by turning this global organisation into an ‘on-demand police service’.We conclude thus: even if you think you are doing the right thing, you must not do the right thing with the wrong process because right is also enshrined in the process. The ends cannot justify the means.
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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 | 0.049 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.055 |
| Scholarly communication | 0.031 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".