Establishing a World Anti-Doping Code : WADA's impact on the development of an international strategy for anti-doping in sport
Bibliographic record
Abstract
The use and prevalence of performance enhancing drugs is not unique to modern sport. Reports of athletes striving to improve their physical abilities date back to third century BCE. Endeavoring to address the growing problem of doping in sport, WADA was created in 1999. This study evaluated how the formation of WADA impacted the development of an international strategy for anti-doping in sport. Conclusions were reached through the analysis of three primary sources of data: personal and organizational archives; media articles; and exploratory interviews. As revealed by the data, the formation of WADA brought together the necessary players to reach a solution for doping in sport. It provided a forum for sport and government to work co-operatively generating ideas and focusing thinking. It led to an awakening within government and sport to the complexities of doping; and it embodied an independent/credible organization while raising/maintaining global awareness and interest in doping. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .J46. Source: Masters Abstracts International, Volume: 45-01, page: 0306. Thesis (M.H.K.)--University of Windsor (Canada), 2006.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 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".