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Record W4400876821 · doi:10.4000/122pa

Un écosystème numérique au service du patrimoine théâtral d’Ancien Régime

2024· article· fr· W4400876821 on OpenAlexaff
Charline Granger, Sara Harvey, Tiphaine Karsenti

Bibliographic record

VenueIn Situ · 2024
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical and Literary Analyses
Canadian institutionsCanadian HeritageUniversity of Victoria
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

Né en 2008, le programme « Registres de la Comédie-Française » (RCF) est l’un des premiers projets en humanités numériques portant sur l’histoire des spectacles anciens. Il repose sur la création d’un écosystème numérique mettant en valeur une partie de la collection d’archives anciennes conservées à la bibliothèque-musée de la Comédie-Française. L’objet de cet article est de présenter les questionnements qui ont conduit à la forme actuelle du programme, en revenant sur les choix et les étapes qui ont jalonné son histoire. Il s’agit aussi de s’interroger sur les apports d’une telle entreprise pour la réflexion critique sur les usages du numérique appliqué aux archives et aux sciences humaines, pour les connaissances et les méthodes en histoire du théâtre, pour la valorisation du patrimoine de la Comédie-Française et du spectacle vivant.

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)0.003
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 score0.275
Threshold uncertainty score0.547

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0070.007
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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
Published2024
Admission routes1
Has abstractyes

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