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Record W3132281315 · doi:10.4000/ges.726

Dénoncer les féminicides des femmes autochtones aux États-Unis et au Canada

2019· article· fr· W3132281315 on OpenAlexaboutno aff
Aurélie Journée-Duez

Bibliographic record

VenueGenre en séries · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

À travers l’analyse de la bande dessinée Deer Woman créée en 2015 par Elizabeth LaPensée (Anishinaabe, Canada), notre article a pour objectif principal d’interroger la notion de « super-héroïne » à l’aune de représentations autochtones engagées. Il s’agit de voir en quoi cette revitalisation du mythe traditionnel de la Femme Cerf (Deer Woman) interroge l’identité en termes de sexe et de genre, la culture et la place des femmes autochtones, en prenant une position radicale face au problème sociétal des féminicides en Amérique du Nord. Nous postulons que la figure de Deer Woman peut aussi être vue aujourd’hui comme une allégorie mettant en garde contre la domination masculine sur les femmes et sur toutes autres formes de vie. Tout d’abord, nous étudierons les caractéristiques formelles de Deer Woman et les mythes fondateurs autochtones qui en sont à l’origine. Nous montrerons ensuite en quoi cette super-héroïne dont les pouvoirs apparaissent après qu’elle a été sexuellement agressée, tend à dénoncer et lutter contre les féminicides touchant les femmes autochtones. Enfin, nous nous demanderons comment cette bande dessinée pourrait laisser entrevoir l’émergence d’une super-héroïne autochtone éco-féministe.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.003
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.288
Teacher spread0.246 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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