Exploring Drama as an Additional Language through Research-based Theatre
Bibliographic record
Abstract
This article explores the social, cultural, and emotional learning that occurred when drama was used with a group of native English speakers and English Language Learners (ELL) to build community. These learners consisted of university Drama in Education students who led a group of elementary students in an after-school drama program in Vancouver, Canada. University of British Columbia (UBC) researchers investigated the potential that drama has to build community with learners from multiple backgrounds and ages. The researchers also examined the potential that theatre methods have to analyze and represent findings discovered within the research data. In reflecting upon the learning that supported the community building, three themes were identified within the data: process and product, negotiation and conflict, and the building of community. ELL Program Leaders' journals were used as data to explore the Program Leaders' perspectives of how the drama program influenced their language acquisition skills and cultural understanding. As the data were analyzed, the researchers transformed recurring themes and significant findings into a dramatic text. This text, created and performed by the researchers at multiple conferences, is integrated into the article. Reflections from the researchers are also shared, along with insights gained while developing and presenting their research-based theatre piece.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".