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Record W3114866116

A qualitative examination of severe disciplinary incidents in men’s soccer

2016· article· en· W3114866116 on OpenAlexaff
Theo Chu, Colin J. Deal, Kurtis Pankow, Shannon R. Pynn, Christine L. Smyth, Nicholas L. Holt

Bibliographic record

VenueURSCA Proceedings · 2016
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDisciplineLeaguePsychologyDescriptive statisticsWitnessPerceptionSocial psychologyApplied psychologyPolitical scienceSociologySocial science
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.364
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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