The influence of participant behaviour on officials' satisfaction
Bibliographic record
Abstract
Despite their critical role, officials have been largely overlooked in the sport psychology literature. It is the purpose of this study to examine how participants' behaviour in the sporting context (i.e., coaches, players, and spectators) influences officials' satisfaction with the event. The Justplay Behaviour Management Program (JBMP) asks all game officials to provide a behavioural conduct rating of the home and away coach, players, and spectators on a scale of 1 Very Good to 5 Very Poor. They also provide a rating of their satisfaction with the game on the same scale. The data collection tool is known as a card. Data were collected by the JBMP from 120 officials in approximately 279 games in three sports (baseball, soccer, and football) which resulted in 1066 cards. Overall, officials were quite satisfied with the games (Ms = 1.52, 1.59, and 1.76 respectively). The six behavioural conduct ratings were then regressed on the satisfaction ratings using a stepwise linear regression for each sport (ps < .000). The influence of participant behaviour on officials' satisfaction differed for each sport. In baseball, the home and away players and coaches, and home spectators were the most influential (R2 adjusted = .78). For soccer officials, all three visiting participants and the home coaches were most influential (R2 adjusted = .42); and for football, only the coaches of both the home and away teams influenced the officials' satisfaction (R2 adjusted = .38). Discussion will revolve around implications for officiating training and the role sport psychology can play in this education.
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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 | 0.009 | 0.036 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".