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Record W2237968855 · doi:10.1080/1612197x.2015.1055290

The motivation behind unsanctioned violence in international rugby: A case study of a former elite player

2015· article· en· W2237968855 on OpenAlexaff
John Kerr

Bibliographic record

VenueInternational Journal of Sport and Exercise Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyEliteSocial psychologyAngerSport psychologyPerceptionApplied psychologyPoison controlFocus groupSuicide preventionQualitative researchSociologyLawMedicineMedical emergencyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

At focus in this case study was the participant's perception of, and personal involvement in, rugby violence. The participant was a 50-year-old ex-international and former British and Irish Lions rugby union player. A qualitative approach informed by reversal theory and incorporating retrospective semi-structured interview procedures with inductive and deductive elements was used to explore a number of topics related to his rugby playing career, including rugby violence. Among other statements related to play and power violence, the participant described his experience of three major incidents involving personal rugby violence. Each incident resulted in injury to the opposing player which required extensive medical attention. Two of the reported incidents supported previous reversal theory-based research findings related to the motivation behind anger violence in sport. The third incident illustrated a type of sport violence not previously identified in reversal theory-based sport research. It was termed “protective” or “supportive” violence, where an athlete comes to the aid, rescue or defence of a teammate. The findings provide valuable new insights on rugby violence, adding to sport psychology's understanding of the motivational processes involved.

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 score0.095
Threshold uncertainty score0.376

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 head0.041
GPT teacher head0.364
Teacher spread0.323 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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