Risk Management Strategies by Stakeholders in Canadian Major Sporting Events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this article is to identify and characterize the use of risk management strategies in major sporting events from the perspective of the organizing committee members and other stakeholders. Two Canadian sporting events—the ISU (International Skating Union) 2006 World Figure Skating Championships and the U-20 FIFA ( Fédération Internationale de Football Association ) World Cup Canada 2007—provided the platform for a comparative case study that was built using archival material and interviews. Key findings included a breakdown of risk management strategy types and an analysis of common strategies used across the various stakeholder groups. Seven risk strategy categories were identified by the various stakeholder groups: reduction, avoidance, reallocation, diffusion, prevention, legal, and relationship management. As a result, a strategic framework for dealing with risk management issues experienced at sporting events is provided.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it