MétaCan
Menu
← Back to cohort
Record W1949824939 · doi:10.7202/1032445ar

Ce qui reste du théâtre dans le film : le « cas » Marguerite Duras

2015· article· fr· W1949824939 on OpenAlexaffvenue
Julie Beaulieu

Bibliographic record

VenueÉtudes littéraires · 2015
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Examiner les aspects formels du théâtre durassien facilite la compréhension du système filmique non conventionnel de Marguerite Duras, car bien avant de se livrer à la pratique du cinéma, l’écrivaine s’est consacrée à la littérature, puis au théâtre. Dans le texte dramatiqueLa Musica(créé en 1965), des éléments cinématographiques attirent d’emblée l’attention. De fait, sa mise en scène préfigure celle d’un cinéma à venir, appuyé sur une rupture entre la bande sonore et les images — une mise en scène cinématographique dont seuls les personnages principaux, une femme et un homme ordinaires, seront présents sur scène, dans un décor minimaliste qui annonce la future « disparition » des personnages aux yeux du spectateur. Quelques meubles et des décorations placés ici et là serviront d’assise aux voix, celles-là mêmes qui donneront naissance au cinéma durassien — un cinéma de la littérature, tel que le suggérait Dominique Noguez, un cinéma de la parole. Notre article propose donc une réflexion sur le rapport singulier qu’entretiennent le texte dramatique et le cinéma dans le contexte de l’adaptation filmique, et plus spécifiquement de la réécriture, qui se situe au carrefour des pratiques, des genres et des discours.

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)0.003
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0090.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.133
GPT teacher head0.281
Teacher spread0.147 · 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
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueÉtudes littéraires→Same topicCultural Insights and Digital Impacts→French-language works237,207→