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Record W4239243322 · doi:10.1163/9781848883376_003

Constructing the Self and the Collective at School through Stories: Plurilingual Theatrical Expression Workshops for Immigrant Adolescent Students in Canada

2015· book-chapter· en· W4239243322 on OpenAlexaboutno aff
Caroline Beauregard

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationExpression (computer science)SociologyPsychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Immigration is a very stressful event in the lives of adolescent students that can transform how they see themselves and others around them. Having a space at school where they can express in a metaphorical and playful way their inner lives, helps them make sense of what they are living and adapt to their new environment. In this sense, theatrical expression represents a very powerful tool for teachers and school mental health professionals in that it allows to share personal stories, in a non-threatening way, by using fabrics, musical instruments and/or theatrical exercises. Offering this type of intervention in the classroom supports immigrant students through the process of adaptation and helps them build a renewed sense of individual and collective identity. In this chapter, the author will examine how immigrant and refugee adolescent students construct their individual and collective identities through theatrical narratives and how this contributes to their psychosocial and school adaptation. To illustrate this point, plurilingual theatrical expression workshops, offered by the Transcultural Research and Intervention Team (Erit) to multiethnic high schools in Montreal (Canada) will be introduced. The Drama Plurality workshops are based on improvisation and no performance from the part of the students is sought. The author will argue that the individual and group stories told in this theatrical space reveal and participate in the construction of a coherent self-identity as well as a collective/classroom identity that both foster the well-being of immigrant adolescent students.

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)0.004
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 score0.547
Threshold uncertainty score0.902

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0230.008
Scholarly communication0.0050.002
Open science0.0030.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.266
Teacher spread0.227 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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