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Record W2068598368 · doi:10.7202/501094ar

« Tu m’aimes-tu » : le Récit en creux d’une passion

2005· article· fr· W2068598368 on OpenAlexvenueno aff
Roger Chamberland

Bibliographic record

VenueÉtudes littéraires · 2005
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Dans cet article, l'auteur s'intéresse aux rapports existants entre la littérature et la chanson. Plus spécifiquement, il aborde la chanson « Tu m'aimes-tu » de Richard Desjardins selon la sémiotique de la réception, prenant en compte le rapport musique et texte. Cette chanson, qui a fait connaître son auteur, forme en creux un récit qui s'intègre à la narration plus générale que suggère les neuf autres pièces de l'album d'où elle est extraite. À partir d'un certain nombre d'unités discrètes relatif à l'espace, au temps et à la diégèse, il est possible de reconstituer le schéma général que soustend la pièce. En suivant le déroulement de la chanson, telle qu'elle se présente à un auditeur lors de la première écoute, l'auteur démonte les mécanismes de construction du sens qui prend forme à partir d'une information minimale contenue dans une chanson.

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)0.006
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.010
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0020.003
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 head0.104
GPT teacher head0.289
Teacher spread0.186 · 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".

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Citations1
Published2005
Admission routes1
Has abstractyes

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