Bibliographic record
Abstract
Introduction: Observational learning is a key tool for improving skilled performances. Sport officials (e.g., referees, umpires, and judges) might glean particular benefits from using observation, as most officials do not engage in traditional practice. Unfortunately, little is known about how observational learning can be of benefit to sport officials. Thus, the purpose of this study was to take an exploratory approach to learn more about sport officials' use of observation. Methods: Participants included 206 sport officials (170 male, 35 female, 1 not specified) from 17 sports (mainly ice hockey, soccer, lacrosse, and volleyball). Sport officials completed a 50-question online survey regarding their use of observational learning. Survey questions revolved around the reasons for using observation (e.g., to learn about positioning or rule application), along with when and how participants used observation (e.g., before versus after competitions; watching an unskilled versus skilled model). Results: Participants used observation most frequently to learn knowledge and application of rules, personality and game management, and fitness and positioning/mechanics. Results revealed that participants preferred to use observation after their competitions, while watching other sport officials in-person, and while observing a skilled model who was correctly executing their tasks. Discussion: In the discussion, we expand on the results, connecting it to previous research in sport officiating or observational learning. Lastly, we offer suggestions for future researchers that should help build our understanding of sport officials' use of observation.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".