Using Mobile 360° Video as a Tool for Enhancing Sport Referee Performance: A Case Study
Bibliographic record
Abstract
Many new video-based technologies (e.g., eye trackers, point-of-view camera) have been integrated into sport referee performance monitoring and training. Mobile 360° video (an omnidirectional video-capture tool affixed to the referee during their performance using a chest harness) provides moving images recorded from a first-person perspective. This case study explored rugby union referees’ and referee coaches’ engagement with mobile 360° video during a viewing of another referee’s performance. Using an other-confrontation interview approach, referees’ and referee coaches’ cognitive activity (interests, concerns, noticing, and knowledge) while viewing mobile 360° video was elicited and studied. Participants experienced heightened immersion in the situation, as well as enhanced discovery and noticing behavior, and they constructed different types of embodied and corporeal knowledge. Using a rugby union setting, this occurred through enhanced perceptual involvement provided by mobile 360° video for reflection on referee positioning and movement, contextual inference about decisions, and sensitivity to player cues and interactions. This study provides preliminary evidence for the utility and acceptability of mobile 360° video as a pedagogical innovation in referee training to enhance referees’ decision making, game management, and reflexivity. Limitations, challenges, and applications of immersive mobile 360° video as a pedagogical tool in rugby union refereeing and other sports are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".