Implementing Readers Theatre as an arts-based participatory research method in exploring Asian-Canadian youth identities
Bibliographic record
Abstract
This article provides an overview of our experiences implementing Readers Theatre within the study “Asian-Canadian youth identities in a pandemic era: Arts-based research”. Led by Dr. Nazilla Khanlou, Principal Investigator (PI), this research uses arts-based methodologies (visual arts and dramatic arts) to explore the impact of the COVID-19 pandemic on Asian-Canadian youth identities (Khanlou et al., 2021). As part of the ongoing research, two virtual Readers Theatre workshops were conducted with 24 Asian-Canadian youth aged 16-24. The participants were divided into four groups of six youth and asked to create Readers Theatre scripts illuminating the impact of the pandemic on Asian-Canadian youth and their wellbeing. Preliminary findings from the virtual workshops highlighted mental health challenges, complex family dynamics, and the resilience of Asian-Canadian youth. The Readers Theatre scripts were rich in cultural references and personal narratives and facilitated meaningful dialogue among youth participants. This method proved effective in capturing the identities and experiences of Asian-Canadian youth during the pandemic, offering valuable insights for youth-centred practice, policy, and scholarship. Keywords: Arts-based methodologies, youth, identity, drama, Readers Theatre, COVID-19.
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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 | 0.039 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.020 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".