A qualitative examination of severe disciplinary incidents in men’s soccer
Bibliographic record
Abstract
The purpose of this study was to examine the number of severe disciplinary incidents involving referees in senior men’s soccer, as well as contributing factors. Such incidents include, but are not limited to, abusive remarks, threats, and deliberate violent conduct. Competition in senior men’s soccer takes place between teams within a tiered structure where players are at least 18 years old. Data collection entailed documentary analysis of disciplinary reports (n = 98) provided by a provincial soccer association. After recording annual frequency, offender’s team, level of competition, and length of suspension, descriptive statistics were calculated. Disciplinary reports showed an increase in frequency of incidents from 4 in 2010 to 27 in 2015. These disciplinary incidents occurred across 80 different teams, with 61.1% of them emerging from lower tiers (ie., tier 3 or lower). Suspensions ranged from 0 to 134 games (M = 18.5, SD = 22.6). These results highlight the distribution pattern and increase in severe disciplinary incidents in men’s soccer. The next phase of the study involves conducting interviews to obtain player perceptions of disciplinary incidents involving referees. Approximately 10 players will be recruited across a range of tiers in a provincial soccer league, and share their experiences based on their direct involvement or as a witness to severe disciplinary incidents. Interviews will explore the antecedents of these disciplinary incidents, which may identify strategies to improve players’ and officials’ well-being through the influence of contextual factors. *Indicates faculty mentor.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".