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Record W3133373337 · doi:10.4000/ges.884

« Délectable Lecter » : réification et (homo-)érotisation des corps masculins dans la série télévisée Hannibal

2017· article· fr· W3133373337 on OpenAlexaff
Hélène Breda

Bibliographic record

VenueGenre en séries · 2017
Typearticle
Languagefr
FieldPsychology
TopicPsychoanalysis and Psychopathology Research
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsHumanitiesPhilosophyNarrativeArtLinguistics

Abstract

fetched live from OpenAlex

Cet article propose d’analyser la manière dont la série télévisée américaine Hannibal (NBC, 2013-2015) représente les corps de ses principaux personnages masculins. Nous formulons l’hypothèse que ce programme remet en question les normes de représentation genrée « traditionnelles », grâce à un processus de réification, et à une (homo-)érotisation de ces corps. Pour le montrer, nous étudierons tout d’abord les différentes techniques, formelles et narratives, ainsi que les choix esthétiques qui transforment les protagonistes en « hommes-objets ». Puis nous aborderons la question de leur érotisation, en montrant que la série a recours à des techniques filmiques habituellement réservées aux personnages féminins, pour faire des protagonistes masculins des objets de désir. Enfin, nous nous poserons la question des points de vue, en montrant que l’œuvre opère un renversement de la notion de male gaze (regard masculin), au profit d’un female gaze et d’un gay gaze.

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)0.002
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: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.017
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.048
GPT teacher head0.393
Teacher spread0.345 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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