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Record W2024705036 · doi:10.1080/2159676x.2011.653499

Narratives of despair and loss: pain, injury and masculinity in the sport of mixed martial arts

2012· article· en· W2024705036 on OpenAlexaff
Dale Spencer

Bibliographic record

VenueQualitative Research in Sport Exercise and Health · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMasculinityMartial artsNarrativeNormativeAthletesAmateurPsychologyEthnographyAestheticsSociologyGender studiesPhysical therapyMedicineLawArtVisual artsLiteraturePolitical science

Abstract

fetched live from OpenAlex

Considerable attention has been paid to the experiences of pain and injury in the sociology of sport literature. In this vein, this paper probes the experience of pain and injury as it pertains to masculinities. This paper documents the specific ways that, on the one hand, athletes conform to masculine ideals through attempts to assume ideal states of embodiment and withstanding pain associated with participation in sport, while on the other hand, through debilitating bodily injury, athletes actually fail to materialise masculine ideals associated with participation in sport. Drawing from an ethnography of mixed martial arts (MMA), this article documents the embodied experiences of pain and injury among MMA fighters. Based on 45 interviews with professional and amateur MMA fighters as well as field notes, this article elucidates the ways fighters interpret their bodily injuries, how this impacts upon masculine identities, and how injuries affect their conformance to the normative masculinity of MMA. This paper focuses on the narratives of despair and loss as it pertains to particular moments in the careers of MMA fighters.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0130.023
Scholarly communication0.0060.005
Open science0.0010.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.249
GPT teacher head0.535
Teacher spread0.285 · 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 designQualitative
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

Citations84
Published2012
Admission routes1
Has abstractyes

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