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Record W4403172181 · doi:10.7202/1112995ar

Lire le cannibalisme dans <i>L’Ingratitude</i> de Ying Chen à la lumière du concept de « manger de l’homme » de Lu Xun

2023· article· fr· W4403172181 on OpenAlexaff

Bibliographic record

VenueMuseMedusa Revue de littérature et d’arts modernes · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMangerHumanitiesArtPhilosophyTheology

Abstract

fetched live from OpenAlex

L’Ingratitude de Ying Chen représente Yan-Zi, qui a l’impression d’être avalée par sa mère patriarcale. Mais le cannibalisme dans ce roman va au-delà de la relation mère-fille. Il partage les trois significations du concept de « manger de l’homme » dans l’oeuvre de Lu Xun, à savoir la pratique de manger la chair humaine, la privation du droit à la vie en raison des codes rituels ainsi que la négation de l’esprit libre individuel et du développement personnel. Dans une société où personne n’est dans un état libre et autonome, Yan-Zi est une révoltée contre ce système cannibale, comme le révolutionnaire Xia Yu dans Le médicament de Lu Xun. Par le cannibalisme, L’Ingratitude établit un dialogue avec Le médicament et dénonce l’héritage nocif des codes rituels de la doctrine confucéenne, qui entraîne la tragédie de Yan-Zi dans l’époque post-Mao en Chine. Le cadre fantastique marque l’originalité de l’oeuvre de Chen, qui emploie une « écriture oblique ». Bien que le cri à la fin des deux oeuvres apporte un petit souffle d’espoir, l’attitude pessimiste ressentie chez Lu Xun trouve un écho dans L’Ingratitude.

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)0.001
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 score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.007
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.002
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 head0.272
Teacher spread0.248 · 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
Published2023
Admission routes1
Has abstractyes

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