Bibliographic record
Abstract
Abstract Conflicts in high-performance sports (HPS) are typically tense and emotionally charged experiences for the athletes, coaches, and sports organizations involved. Such disputes raise intriguing challenges for the mediators handling them. These disputes typically involve multiple parties who often have intensely competitive personalities negotiating a volatile mix of high-stakes win/lose issues. Mediators typically confront numerous process challenges and must operate within the rigid policy parameters of the various governing organizations involved. Mediation can successfully manage and resolve these challenging disputes, often in creative ways that repair and preserve the parties’ relationships. To be successful in this environment, however, mediators must adapt to and confront the unique dynamics of sports disputes described here. In this article, I examine multiple case studies of mediations conducted through the Sport Dispute Resolution Centre of Canada (SDRCC) with the goal of identifying successful mediation strategies for HPS disputes. The centre, which has made mediation mandatory for almost all cases, had an overall settlement rate over a twelve-year period of 46 percent, with rates as high as 94 percent for mediations voluntarily requested by the parties. Mediation has been used only sparingly elsewhere in the world for resolving HPS disputes to date, although, I argue, it is a successful tool that should be increasingly used both nationally and internationally. In recognition of mediation's potential role, the Court of Arbitration for Sport introduced updated mediation rules in 2016 and is moving to increase the use of mediation in international sports disputes.
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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 | 0.028 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.017 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".