Tellers, Makers, and Holders of Stories: A Micro-Analytic Understanding of Students’ Identity Work in Drama-based Adult ESL Classrooms
Bibliographic record
Abstract
Despite a wide-spread pedagogical interest and scholarly conviction in the possibilities of educational drama for creating more contextually-situated, engaging, and multi-modal L2 learning experiences (Piazzoli, 2018; Stinson & Winston, 2011), there is scarce empirical evidence concerning what is actually taking place interactionally in L2 classrooms for adults. This article presents a bottom-up microanalysis of classroom interaction in an ESL class in Canada with over 16 adult learners designed to explore the potential and actual impact of educational drama on classroom discourse and students’ L2 learning experiences. Using a discourse analytic approach (Antaki & Widdicombe, 1998; Goffman, 1981), I analyze the dynamic identity work of the class participants. The article presents empirically-grounded research findings that illustrate instances of interaction in and through which drama-based ESL pedagogy contributes to the development of dialogic and democratic classroom discourse and fosters a transformative empowering interpersonal space (Cummins, 2011).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".