Translanguaging drama: embracing learners’ <i>perezhivanie</i> for embodiment of the repertoire
Bibliographic record
Abstract
The creative and dynamic practices that multilinguals perform with linguistic and non-linguistic features such as the body, movement, senses, and space have been documented as integral to their repertoire. Drawing on interdisciplinary literature, this article advances the concept of the repertoire through translanguaging drama as a pedagogical practice and examines the resources that can be volitionally mobilised through language learners’ perezhivanie, or the emotional, felt and lived through experience. Translanguaging drama was implemented in two English language programmes with adult learners in Canada. While these courses focused on improving English language skills, translanguaging drama was implemented to activate learners’ perezhivanie while using their repertoire, which not only facilitated communication in the English language but pushed for agency in using non-linguistic resources. I examine learners’ perezhivanie with a subset of data, which included observation notes and learner diary entries. In this article, I emphasize four main interrelated areas: (1) volition and empowerment, (2) meaning-making across languages, (3) embodiment of language – through voice modulation, facial expression, and body language–, and (4) language choice triggered by perezhivanie. Implications of the study for furthering the theorisation of language, language learning and the repertoire are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".