MétaCan
Menu
Back to cohort
Record W1981744005 · doi:10.7202/018163ar

Jeux et enjeux de l’énonciation humoristique : l’exemple des Caves du Vatican d’André Gide

2008· article· fr· W1981744005 on OpenAlexvenueno aff
María Dolorès Vivero García

Bibliographic record

VenueÉtudes françaises · 2008
Typearticle
Languagefr
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Pour Ducrot (1984 : 213), qui définit l’humour comme une forme d’ironie, l’énonciation humoristique se caractérise par une dissociation entre le locuteur et l’instance qui prend en charge la position exprimée dans l’énoncé même. On examinera d’abord cette définition dans une perspective discursive pour montrer que ce débrayage ou désengagement énonciatif est inséparable d’un engagement lié à l’acte d’humour, qui cherche la connivence du lecteur. Il est indissociable également de l’engagement lié à une visée critique souvent sous-jacente à la stratégie humoristique. Nous présenterons ensuite une analyse énonciative de l’humour dans Les caves du Vatican d’André Gide, en prenant appui sur les catégories discursives de l’humour établies par Charaudeau (2006) en fonction des positions énonciatives non prises en charge par l’instance qui apparaît comme responsable de l’énoncé. On soulignera en particulier, dans cette analyse, comment le désengagement humoristique peut rendre plus efficace une écriture engagée.

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)0.007
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 score0.053
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.030
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0040.006
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 head0.053
GPT teacher head0.290
Teacher spread0.237 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueÉtudes françaisesSame topicLinguistics and Discourse AnalysisFrench-language works237,207