Network governance of a multi-level, multi-sectoral sport event: Differences in coordinating ties and actors
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
To understand how partners within a large, multi-sectoral network coordinated amongst one another, this paper empirically determined stakeholders’ network capital vis-à-vis centrality by focusing on the relationships within the Vancouver 2010 Olympic Winter Games. An embedded case study was built using 6382 pages of documents (e.g., meeting minutes, memos, newspaper articles, and annual reports) and 55 interviews, and analyzed using social network analysis. The results revealed actors used eight types of ties in their coordination efforts: collaboration, communication, coordinating bridge, instrumental, legal, regulatory, transactional, internal link, and external link. Also, highly centralized actors were context specific to each level of government, with the organizing committee and federal secretariat emerging as the most critical for coordination efforts. Findings empirically demonstrate the importance of the national/federal government to coordinate multi-sectoral sport event networks. Thus, sport event partners can consider structuring an event’s network administrative organization to fit their differing strategic goals.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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