Enhancing the Fan Experience at Live Sporting Events: The Case of Stadium Wi-Fi
Bibliographic record
Abstract
While live sport event attendance remains a pervasive and progressing issue for university athletic programs, athletic directors should consider strategies to boost perceptions of stadium innovativeness. Professional sport leagues have pursued the adoption of Wi-Fi capabilities much more aggressively than their collegiate sport counterparts. This case introduces the Wi-Fi adoption issue for collegiate sport including a conversation surrounding the foundational technical aspects of Wi-Fi and cellular data for sport venues, the current status of Wi-Fi for collegiate athletics in comparison with professional leagues, and the benefits and drawbacks of Wi-Fi adoption. Case participants are asked to evaluate the merits of Wi-Fi adoption for a “Power Five” institution from the position of the athletic director. Participants will address adopting functional technology for the rise in college esports, Wi-Fi inclusion for indoor and outdoor venues, and comparative analyses among connected and disconnected stadiums.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".