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Record W4285314814 · doi:10.35921/jangada.v1i18.380

QUAND LES FEMMES PRENNENT LES ARMES : L’ÉCRITURE DE LA VIOLENCE FÉMININE DANS LA FANTASY MÉDIÉVALISANTE

2021· article· fr· W4285314814 on OpenAlexaff
Cassandra Simon, Antoine Geslin

Bibliographic record

VenueJangada crítica | literatura | artes · 2021
Typearticle
Languagefr
FieldArts and Humanities
TopicLiterature and Culture Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Au regard des nombreuses œuvres du genre, la fantasy médiévalisante et la violence semblent intrinsèquement liées au travers des enjeux de pouvoir et de domination qui caractérisent les structures sociales et les relations interpersonnelles mises en texte par le récit. Bien que la violence soit traditionnellement rattachée à la masculinité, la multiplication de combattantes dans les fictions contemporaines annonce sa féminisation. L’étude du roman de Manon Fargetton, Les Illusions de Sav-Loar, nous permet cependant de repérer une tension dans la violence féminine, dont la représentation ne va jamais de soi. Soumise au cadre du patriarcat, la violence des femmes est subordonnée à celle des hommes et sa légitimation dépend des modalités de son expression. La vengeance et l’autodéfense, en tant qu’actes isolés et individuels, sont ainsi approuvées par le texte tandis que la violence révolutionnaire qui vise à transformer l’organisation de la société subit la condamnation de la narration et des protagonistes du récit. Finalement, Les Illusions de Sav-Loar suggèrent que le véritable pouvoir féminin se situe dans la non-violence, plus proche de la nature féminine pacifique et maternelle, comme réponse ultime et conciliatrice à la domination brutale des hommes : les combattantes troquent alors le glaive contre l’enfant.

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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.016
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.024
GPT teacher head0.258
Teacher spread0.235 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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