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Record W2730176535 · doi:10.4000/belphegor.881

Rapports de pouvoir : race, genre et nation dans la couverture montréalaise des JO de Berlin

2017· article· fr· W2730176535 on OpenAlexvenueaboutno aff
Camille Caron Belzile, Ève Léger-Bélanger, Alex Giroux, Marilou St-Pierre, Micheline Cambron, Dominique Marquis

Bibliographic record

VenueBelphégor · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Le sport est depuis toujours un facteur de hiérarchisation des groupes sociaux. Dans une perspective de rapport de pouvoir, il sert tout autant à réaffirmer les diverses relations inégalitaires entre les groupes à l’intérieur d’une société qu’à poser ou illustrer les relations conflictuelles entre nations. Le présent texte explore la façon dont les journaux participent à l’interprétation et à la construction de ces rapports dans le cadre de la couverture des Jeux olympiques de 1936. L’analyse aborde d’abord la question de la race, particulièrement la « mise en valeur » des athlètes noirs. On découvre ainsi que, paradoxalement, le racisme ambiant permet la mise en place d’un débat de fond sur la question. La réflexion se poursuit ensuite avec les rapports de genre en montrant comment les journaux marginalisent les accomplissements des sportives afin d’éviter une remise en question des schémas traditionnels. Finalement, les Olympiades sont aussi l’occasion pour les journaux de construire un récit national fort à travers diverses stratégies discursives. À bien des égards, les JO permettent de construire une identité canadienne qui cherche à transcender les régionalismes. L’omniprésence de l’expression de ces rapports de pouvoir offre néanmoins aux journalistes l’occasion de réfléchir à ces questions.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.162
Threshold uncertainty score0.326

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.007
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.033
GPT teacher head0.308
Teacher spread0.275 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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