MétaCan
Menu
Back to cohort
Record W2994664552 · doi:10.4000/itineraires.7091

La scène, l’écran : questionnements identitaires et tensions du désir dans Tom à la ferme de Michel Marc Bouchard et de Xavier Dolan

2019· article· fr· W2994664552 on OpenAlexaboutno aff
Stefano Genetti

Bibliographic record

VenueItinéraires · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicMilitary, Security, and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanitiesPhysicsPhilosophy

Abstract

fetched live from OpenAlex

Écrite en 2010 et créée à Montréal en 2011, la pièce en douze tableaux Tom à la ferme du dramaturge québécois Michel Marc Bouchard a été portée à l’écran en 2013. L’auteur lui-même a travaillé au scénario en collaboration avec le réalisateur, coproducteur et interprète du film Xavier Dolan. C’est ainsi que ce drame de l’homophobie et du mensonge intériorisé, riche en dispositifs métathéâtraux et alternant la violence et le comique, devient un thriller psychologique de la domination érotisée. Comportant plusieurs déplacements d’accents narratifs et esthétiques, cette adaptation donne lieu, dans cet article, à une comparaison concernant la manière dont la pièce, puis le film, posent les questions de l’identité culturelle, linguistique et homosexuelle. Dans le passage de la scène à l’écran, la mystification existentielle cède le pas aux fluctuations de la répulsion et de la fascination indémêlables : de l’affirmation d’une identité problématique on glisse vers la problématisation de toute affirmation identitaire.

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 score0.910
Threshold uncertainty score0.178

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 studies0.0100.012
Scholarly communication0.0080.004
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0110.001

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 head0.021
GPT teacher head0.338
Teacher spread0.317 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueItinérairesSame topicMilitary, Security, and Education StudiesFrench-language works237,207