Towards the Adoption of e-Refereeing and e-Ticketing in Elite Soccer Championships: an Institutional Perspective
Bibliographic record
Abstract
Although the role of IT is salient in sporting mega-events (e.g. instant replay, goal-line technology), IS research has not yet paid any attention to the processes by which technologies are selected for and implemented at these mega-events, the strategies used by actors, nor the consequences of such implementations on actors in related sectors and industries. To tackle this underdeveloped research topic, we focus on the last three UEFA (Union of European Football Associations) soccer championships (2000, 2004, 2008) and we describe how some technologies are adopted (e.g., e-ticketing) while others (e.g., e-refereeing) are not. With an overall goal of deepening our understanding of IT-related institutional work surrounding mega-events in general and sporting mega-events in particular, our objective is to better comprehend the role and actions of institutional entrepreneurs in the selection and implementation of IT for sporting mega-events.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".