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Record W1923595664 · doi:10.1177/1012690214547373

Assessing the sociology of sport: On sports violence and ways of seeing

2015· article· en· W1923595664 on OpenAlexaff
Kevin Young

Bibliographic record

VenueInternational Review for the Sociology of Sport · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSociology of sportSociologyCriminologyGlobeFootballDeviance (statistics)Social scienceMedia studiesLawPolitical sciencePsychology

Abstract

fetched live from OpenAlex

On the 50th anniversary of the ISSA and IRSS, a scholar central to the understanding of deviance and violence in sport, Kevin Young, considers the trajectory, challenges and future for research on sport violence. He notes that the sociology of sports violence has been surprisingly limited in its scope, with focus often on football hooliganism and ice hockey tactics. In considering the challenges of sport-related violence, Young notes confounding issues of access and candor in reporting, and interrogates Berger’s notion of ‘ways of seeing’ to provoke new lenses for approaching and understanding the cultures and contexts of violence in sport. In addition, it is argued that using an approach that combines criminology, social justice and community health concerns is crucial to sociological inquiry. The essay closes with key arguments for the study of sport-related violence to ‘open the lens’ by looking beyond the ‘predictable crucibles’ of the UK and North America to examine the permutations of culturally embedded violence that link to sport across the globe.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0080.053
Scholarly communication0.0130.019
Open science0.0010.010
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.111
GPT teacher head0.406
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations13
Published2015
Admission routes1
Has abstractyes

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