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Record W4396855776 · doi:10.26522/jess.v10i.4547

Sparring with Femininity

2024· article· en· W4396855776 on OpenAlexaffvenueabout
Emma Balazs, Jordan Koch

Bibliographic record

VenueJournal of Emerging Sport Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsMcGill University
Fundersnot available
KeywordsRacializationFemininityGender studiesEthnographyMartial artsSociologyMasculinityProfiling (computer programming)PsychologyRace (biology)ArtVisual artsAnthropology

Abstract

fetched live from OpenAlex

This ethnography explores how a group of adolescents—predominantly girls—negotiated a sport-for-development program centred on the traditionally masculine pastime of mixed martial arts (MMA). The ethnographic setting was a youth centre in central Montréal that provides free after-school programming for youths aged 11 to 18 years. Our analysis yielded three prominent themes: 1) ‘girls don’t do MMA,’ in which we discovered how young female pugilists actively challenged residual stereotypes that cast them as ‘too soft’ to play violent sports such as MMA; 2) ‘a mixed bag of martial artists,’ in which we learned how local youths conceived MMA as a vehicle for supporting innovative agendas that extended well-beyond the sport of MMA; and 3) the ‘racialization of self-defence,’ in which we witnessed how self-defence against complex social realities (profiling and marginalization) experienced by many non-white youths in the area were integrated into the teachings of the MMA program. Collectively, these themes raise important questions about the socially significant role(s) that MMA plays in the lives of local youths with respect to their race, class, and gendered identities.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.072
GPT teacher head0.382
Teacher spread0.310 · 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 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

Citations0
Published2024
Admission routes3
Has abstractyes

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