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Record W3172683853 · doi:10.37119/ojs2021.v26i2.487

The French Play: An Ethnodrama About Applied Theatre for Social Justice Education in Middle School

2021· article· en· W3172683853 on OpenAlexaffvenue
Allison Balabuch

Bibliographic record

Venuein education · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicCreative Drama in Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSocial justicePedagogyThe artsSpace (punctuation)SociologyPsychologyVisual artsSocial scienceArtComputer science

Abstract

fetched live from OpenAlex

In this study, I investigate the use of applied theatre with French Immersion Grade 8 students to better understand social justice issues. Through unstructured interviews, four participants were asked to recall their past experiences participating in applied theatre projects as a learning experience and as a process to better understand social justice issues. Participants’ words and feedback were then used to create an ethnodrama script, performed by the participants and me via video conference. Findings are grouped under five categories; Doll’s (2013) 4Rs: Richness, Rigor, Recursion, and Relations and Freire’s (2000) concept of conscientization. Participants in applied theatre reported they had a space to tell authentic stories in their own words, became more self-confident, and work towards being catalysts for change. The purpose of this article is to inform educators of the possibilities in using applied theatre and problem-posing education for social justice education and to share the process of ethnodrama as a methodology in arts-based research. Keywords: applied theatre; ethnodrama; problem-posing education; social justice education

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0140.016
Scholarly communication0.0070.004
Open science0.0020.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.039
GPT teacher head0.324
Teacher spread0.285 · 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 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

Citations1
Published2021
Admission routes2
Has abstractyes

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