MétaCan
Menu
← Back to cohort
Record W4405611182 · doi:10.7202/1114865ar

« Combattre d’égale à égal »

2024· article· fr· W4405611182 on OpenAlexvenueno aff
Jérôme Beauchez, Josselin Mattont

Bibliographic record

VenueAnthropologie et Sociétés · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Au tournant des années 2000, les ethnographes ont investi les sports de combat — à commencer par la boxe — comme un laboratoire d’étude des dominations masculines et des résistances féminines éprouvées dans l’expérience du corps-à-corps. Une majorité d’études a souligné la subalternité paradoxale des combattants, aussi souvent dominants au regard de leurs aptitudes aux affrontements physiques que dominés en termes de position socio-raciale. La description de ces masculinités dominantes/dominées a dès lors guidé celle des hégémonies masculines dans les sports de combat. Par contraste, les ethnographes qui ont décrit les féminités combattantes les ont souvent observées et comprises du point de vue d’un « féminisme physique » majoritairement promu par des femmes blanches issues des classes moyennes intellectuelles. Qu’en est-il des combattantes subalternisées, de leur engagement physique et du sens qu’elles donnent à leurs épreuves de l’adversité, dans et au-delà du corps-à-corps ? C’est tout l’enjeu de cet article que d’apporter des réponses à cette question, aussi insuffisamment documentée par l’ethnographie que propice à interroger non seulement les féminités, mais le « féminisme pragmatique » des combattantes qui résistent quotidiennement aux dominations — de genre, de classe et de race — au travers de leur engagement dans une lutte inséparablement physique et sociale.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.021
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.012
Scholarly communication0.0050.004
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0140.002

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 head0.246
GPT teacher head0.564
Teacher spread0.318 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAnthropologie et Sociétés→Same topicSports, Gender, and Society→French-language works237,207→