Referee Decision Making in a Video-Based Infraction Detection Task: Application and Training Considerations
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
This study addressed factors that influence referee decision making in basketball. Four different groups of basketball officials were shown video clips testing their ability to detect fouls and violations (infractions). In a knowledge-priming condition, referees were given a rules test before infraction detection. In an infraction-priming condition, referees were instructed to focus on defensive fouls. The results did not show clear effects of knowledge or infraction priming. This implies that neither a pre-game review of the rules or league recommendation, nor the common coach behaviour of asking a referee to focus on a particular infraction influence performance in the calls that are made. Rather, the results indicate that detecting infractions in video clips may be influenced by features of the video tool. Performance is influenced by the specific clips and their format sequencing. These findings illustrate the complexity of referee decision-making, and provide guidance for designing coaching tools for this skill. In particular, this research suggests that referee decision-making tools progress in perceptual difficulty (e.g., on-the-ball to off-the-ball infractions)
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it