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Record W2580936246 · doi:10.7202/1038509ar

Les « favoris de Mars1 ». Gens d’épée et comédie fin de règne (1680-1715)

2016· article· fr· W2580936246 on OpenAlexaffvenue
Marie-Ange Croft

Bibliographic record

VenueTangence · 2016
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Véritables phénomènes de société en raison des nombreuses guerres que mène Louis xiv tout au long de son règne, les gens de guerre ne pouvaient espérer demeurer à l’écart de la critique théâtrale. Dès la décennie 1670, ils s’invitent sur les planches, notamment chez les Italiens, et connaissent une grande fortune littéraire à partir de 1680. Rarement identifiés comme tel dans la liste des personnages — ce qui explique par ailleurs l’absence de travaux sur le sujet —, ils apparaissent sous diverses appellations : cadet, capitaine, chevalier, dragon, fantassin, homme de guerre, major, militaire, mousquetaire, officier, sergent, soldat, spadassin et, par extension parfois, Gascon. La mise en scène du soldat dans la comédie dite fin de règne, les préjugés et les valeurs qu’il incarne attestent des représentations ambivalentes que s’en fait la société fin de règne. En s’appuyant sur un corpus composé d’une quarantaine de comédies tirées du théâtre italien et français, qui mettent en scène un ou des hommes de guerre, cet article entend mieux cerner les caractéristiques et les fonctions dramaturgiques de cette figure complexe.

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)0.002
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 score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.005
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0200.002

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 head0.044
GPT teacher head0.257
Teacher spread0.213 · 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
Published2016
Admission routes2
Has abstractyes

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