An Agency Theory Perspective on Corruption in Sport: The Case of the International Olympic Committee
Bibliographic record
Abstract
This article discusses agency problems in sport organizations in which the same individuals are involved in both the management and control of decision making. We focus our analysis on the case of the International Olympic Committee (IOC) by reviewing the behavior of selected IOC members with regard to the bidding process for the Olympic Games and the resulting reform attempts made by the IOC in an effort to address issues of corruption. After a review of examples of corrupt behavior on the part of IOC members, agency theory is introduced to discuss IOC reforms and provide some suggestions for future reform. We propose incorporating other stakeholders (in addition to the IOC members), such as corporate partners, media conglomerates, and other members of the Olympic movement (e.g., athletes, coaches, officials), into management and control functions. More specifi cally, it is suggested that these stakeholders comprise a board that oversees the operations of the IOC (similar to the IOC’s current executive committee) and be given the ability to remove and/or sanction IOC members who act self-interestedly to the detriment of the Olympic movement. Thus, by delegating the control function of decision making to a board and the management function to internal agents, greater accountability for all organization members can be achieved.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".