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Record W3022433829 · doi:10.26522/vp.v17i1.2467

Emma Bratte, la femme folle du Livre d’Emma

2020· article· fr· W3022433829 on OpenAlexvenueno aff
Pooja Booluck

Bibliographic record

VenueVoix Plurielles · 2020
Typearticle
Languagefr
FieldPsychology
TopicPsychoanalysis and Psychopathology Research
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Emma Bratte, admise dans un hôpital psychiatrique, est accusée d’avoir tué sa fille. Frustré dans ses efforts pour tirer de sa patiente l’information qu’il cherche, le psychiatre blanc invite une interprète, Flore, elle-même d’origine haïtienne, à déchiffrer les paroles et mémoires de cette criminelle. À travers le discours oral d’Emma, Flore cherche à comprendre les souffrances de cette dernière ainsi que les motifs du meurtre de sa fille. L’article interroge le sujet de la folie et de la femme antillaise à partir du roman Le livre d’Emma de Marie-Célie Agnant. Quelles sont les raisons principales pour lesquelles Emma a coupé sa lignée générationnelle en tuant sa progéniture ? Ce crime s’explique-t-il par la cruauté de cette femme, par sa condition mentale ou par une décision calculée en fonction de la réalité étouffante du passé ? Ce travail expose et réévalue les préjugés au sujet de la femme antillaise dite « folle », une femme qui met sa parole au service de la mémoire troublante d’un passé collectif toujours présent. Mots clés : Folie; infanticide; littérature antillaise; colonialisme; racisme; mémoire

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)0.005
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0110.007
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0140.003

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 head0.041
GPT teacher head0.324
Teacher spread0.283 · 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
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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