MétaCan
Menu
Back to cohort
Record W3111208929 · doi:10.3138/tric.41.2.a02

<i>Traversée</i>: Crossing Borders in Search of the Emancipatory Theatre for Children

2020· article· fr· W3111208929 on OpenAlexaffvenue
Yana Meerzon

Bibliographic record

VenueTheatre Research in Canada · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicChina's Global Influence and Migration
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesPublicsArtPolitical sciencePhilosophyPoliticsLaw

Abstract

fetched live from OpenAlex

Comment le théâtre doit-il parler aux enfants de politiques d’immigration, de la mort, de la séparation, de l’arrivée dans un nouveau pays et de nouveaux espoirs? Quel ton doivent adopter les artistes pour parler de violence et de torture, d’injustice, de manipulation politique, ou pour dire la perte de sa famille et de sa langue, la quête de sécurité et la recherche de nouveaux amis? Cette conversation doit-elle être pédagogique, protectrice, prudente, neutre ou divertissante? Comment les artistes doivent-ils façonner leur œuvre destinée à de jeunes publics pour les aider à saisir des sujets si complexes? La pièce Traversée (2011) d’Estelle Savasta fournit quelques pistes de réponse à toutes ces questions. Elle tient compte des traditions pédagogiques et artistiques du « théâtre de l’émancipation pour enfants » et favorise une conversation sur la guerre et la migration. La mise en scène par Milena Buziak de cette pièce en 2016 est un exemple de théâtre pour jeunes publics qui assume la responsabilité de conscientiser son auditoire aux réalités politiques, notamment en ce qui concerne des injustices sociales contemporaines. En abordant des questions politiques urgentes liées à la migration mondiale, Yana Meerzon montre comment on peut faire du théâtre jeunesse un instrument politique.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.028
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0180.023
Scholarly communication0.0130.008
Open science0.0010.008
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0130.002

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 head0.078
GPT teacher head0.389
Teacher spread0.311 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueTheatre Research in CanadaSame topicChina's Global Influence and MigrationFrench-language works237,207