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Record W1507800648 · doi:10.26522/vp.v3i1.525

« Li vallés qui est mescine » - Ambiguïté du masque, ambiguïté de la parole dans Le roman de Silence

2006· article· fr· W1507800648 on OpenAlexaffvenue
Gabriela Tănase

Bibliographic record

VenueVoix Plurielles · 2006
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsCouncil of Ontario UniversitiesUniversity of Toronto
Fundersnot available
KeywordsHumanitiesArtSilence

Abstract

fetched live from OpenAlex

Le Roman de Silence est par excellence un récit des ambiguïtés. Ambiguïté de l’identité, à travers les travestissements de l’héroïne, ambiguïté du langage, qu’un narrateur rusé fait osciller entre les catégories du masculin et du féminin, ambiguïté des sens, où tout message devient contradictoire. Il m’a paru ainsi intéressant de proposer un article sur l’oeuvre d’Heldris de Cornouälle, cet auteur dont l’identité nous échappe également, en pensant que le motif du changement d’identité, fortement lié à l’incertitude d’un discours confondant le sérieux et le comique, illustrera de manière inédite le thème de la diversité. La plupart des études consacrées au Roman de Silence partent en effet de l’ambiguïté sexuelle de Silence, la protagoniste du roman, pour déboucher sur le tissu intriqué d’une histoire où aucune parole n’est stable. L’originalité de mon travail tient dans la façon d’articuler l’incertitude identitaire et discursive en fonction du topos du masque, essentiel dans la littérature médiévale.

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)0.004
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 score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.014
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.002

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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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
Published2006
Admission routes2
Has abstractyes

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