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Record W2119230826 · doi:10.1123/ssj.24.2.127

Sport, Sexuality, and the Production of (Resistant) Bodies: De-/Re-Constructing the Meanings of Gay Male Marathon Corporeality

2007· article· en· W2119230826 on OpenAlexaffabout
William Bridel, Geneviève Rail

Bibliographic record

VenueSociology of Sport Journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGender studiesSociologyAppropriationHuman sexualityHegemonyCONTESTResistance (ecology)Power (physics)Context (archaeology)Discourse analysisSocial constructionismAestheticsEpistemologySocial sciencePoliticsArt

Abstract

fetched live from OpenAlex

Placing the sporting body and Michel Foucault’s technologies of power and of the self at the center of our research inquiry, this article explores the ways in which 12 Canadian gay male marathoners discursively construct their bodies within and beyond the marathon context. Thematic analysis of the research materials (gathered through guided conversations, written stories, and the first author’s research journal) revealed four main themes: self-governed bodily practices, body modification, the marathoning body as resistant to dominant representations of male corporeality in gay culture, and transformative potential. Following Foucault, materials were further submitted to discourse analysis through which we uncovered the appropriation of and resistance to dominant discourses. This analysis suggested the subjects’ discursive constructions as “hybrid” creations located both within, and sometimes in contest to, dominant discourses of physical activity, running, and the male body in gay culture. Our research explores the experiences of gay male athletes through a sociological lens that differs from the present literature, which has largely drawn on hegemony theory. It also adds new insights into distance running as a social phenomenon.

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)0.018
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.009
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 head0.042
GPT teacher head0.326
Teacher spread0.284 · 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.

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

Citations76
Published2007
Admission routes2
Has abstractyes

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