Coordination in International and Domestic Sports Events: Examining Stakeholder Network Governance
Bibliographic record
Abstract
This paper sought to examine the stakeholder network governance structures of two international and two domestic multisports events focusing on (a) exploring the structural connectedness of these networks and (b) illuminating powerful stakeholders vis-à-vis centrality and the ability to control the network’s flow. An exploratory, comparative case study design was built by means of 58 interviews and 550 archival materials. Findings highlight international sports events are sparsely connected networks with power concentrated in the organizing committee, government, and venue stakeholders, who broker coordination with other stakeholders. In contrast, domestic sport event organizing committees appear more decentralized as coordinating actors: Sport organizations, sponsors, and community-based stakeholders emerged as highly connected, powerful stakeholders. Domestic event governance decentralization highlights a potential imbalance in stakeholder interests through network flow control by multiple actors, while the governments’ centrality in international events demonstrates not only mode-dependent salience but also visibility/reputational risks and jurisdictional responsibilities-based salience.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".