Creating a Corporate Anti-doping Culture: The Role of Bulgarian Sports Governing Bodies
Bibliographic record
Abstract
The World Anti-Doping Agency's (WADA) vision to promote a new moral order in sport and new forms of organization and management through the World Anti Doping Code (WADC) amount to creating a new corporate culture. The WADC's emphasis on policy implementation places sport governing bodies (SGBs) and managers at the heart of the enterprise. This represents a double challenge: (i) to the organizational culture of SGBs as it entails creating shared systems of meaning that are accepted, internalized, and acted on at every level of an organization, and (ii) to the International Olympic Committee (IOC) and the WADA in regard to universality and particularity, where the general organizational difficulty is how they are to operate at a global (universal) level whilst such apparently intractable differences exist at the particular (local) level. This essay employs Morgan's metaphor of organizations as cultures to develop an understanding of the process of endorsing a global anti-doping policy. It explores the enactment of the WADC using the Bulgarian Weightlifting Federation as a case in point. While a good level of universal approval of the WADC has been achieved, the main issue remains how to get SGBs' practices in line with it.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".