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Record W2278788332 · doi:10.33137/rr.v38i4.8838

Marguerite de Navarre et le mystère médiéval

2002· article· en· W2278788332 on OpenAlexvenueno aff
Charles Mazouer

Bibliographic record

VenueRenaissance and Reformation · 2002
Typearticle
Languageen
FieldArts and Humanities
TopicRenaissance Literature and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsLyricismLiteratureFifteenthRepresentation (politics)PoetryArtFaithSermonHumanitiesHistoryPhilosophyTheologyClassics

Abstract

fetched live from OpenAlex

This article examines the relationship between Marguerite de Navarre’s biblical plays and the genre of the fifteenth- and sixteenth-century medieval mystery play by focusing on three dramatic elements: staging, characters, and language. While, with respect to staging, Marguerite recaptures the structure of the mystery play, she takes less interest in the representation of the movements of the characters and in the realistic scenes, which were central to this medieval genre; as she reduces the dramatic events and their dynamics, she concedes little to the performance, privileging hearing over seeing. Regarding the characters, Marguerite also moves away from the conventions of the mystery play, according to which characters were endowed with familiar, concrete, human traits. Except for a few conspicuous cases, the queen of Navarre shows a preference for symbolic, abstract entities, or for human characters so disembodied that their faith or holiness takes them away from the human world and closer to the heavens. As for the language of her plays, Marguerite creates a poetic rhythm that almost depletes the life and suppleness of the dialogue. Thus the vivacity of the verbal exchanges is transcended to the advantage of other styles that are less suited to the genre of theater, with the exception of the sermon and spiritual lyricism. In conclusion, Marguerite, in her four biblical plays, undermines the medieval form of the mystery play, which, however, she also recaptures.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.865
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.210
Teacher spread0.192 · 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 teacher head, 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
Published2002
Admission routes1
Has abstractyes

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