Factors contributing to severe discipline incidents in men's soccer
Bibliographic record
Abstract
Disciplinary incidents (i.e., verbal or physical abuses of officials) in men's soccer in Alberta have been increasing steadily over the past 5 years (Deal et al., 2016). We were asked by the provincial soccer association to conduct a study to better understand these disciplinary incidents. Hence, the purpose of this study was to examine the factors which contribute to severe disciplinary incidents in men's soccer. Semi-structured interviews (M = 50 minutes, SD = 21.3 minutes) were conducted with 22 participants who were members of three groups: disciplinary committee members (n = 3; Mage = 54 years, SD = 6.51 years), referees (n = 9; Mage = 47 years, SD = 12.75 years), and players (n = 10; Mage = 22 years, SD = 1.90 years). Thematic analysis was used to identify nine factors that contributed to severe disciplinary incidents. These factors were broadly organized around an ecological framework, ranging from distal to more proximal issues. Sociocultural factors included themes of culture (influences of soccer and family culture) and discrimination. Organizational factors represented themes of organizational structure (PSO rules and policies) and procedural issues pertaining to disciplinary hearings. Contextual factors included the physical environment (indoor versus outdoor soccer) and game characteristics (close game, history between teams). Individual factors included the attitudes, behaviors, and knowledge of coaches, players, and referees. The next step in this research will involve working with the PSO to design ways to intervene at different ecological levels in order to ultimately reduce the number of disciplinary incidents in the future.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".