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Record W3160308055 · doi:10.7202/1076683ar

Enseigner le métier d’acteur·trice : expériences de formation du corps et approches privilégiées

2021· article· fr· W3160308055 on OpenAlexaffvenueabout
Marie-Eve Skelling Desmeules

Bibliographic record

VenuePercées Explorations en arts vivants · 2021
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesSociologyArt

Abstract

fetched live from OpenAlex

Cet article porte sur les stratégies et les approches axées sur le travail corporel privilégiées par cinq professeur·es (Francine Alepin, Martine Beaulne, Pascal Belleau, Huy Phong Doan et Mélanie Demers) dans le cadre de différents cours de voix, d’interprétation et de mouvement offerts à l’École supérieure de théâtre (ÉST) de l’Université du Québec à Montréal (UQAM). En m’appuyant sur la théorie de l’activité (Engeström, 2000), je tisse des liens entre les diverses composantes (sujet, objet, communauté, règles, division du travail, outils, finalité) associées à ces contextes de cours et j’approfondis leur finalité commune d’autorégulation vis-à-vis du travail du corps, un corps à la fois charnel, performant, personnifiant et en relation avec d’autres.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.003
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 head0.190
GPT teacher head0.397
Teacher spread0.207 · 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.

Study designQualitative
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
Published2021
Admission routes3
Has abstractyes

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