Bibliographic record
Abstract
In this editorial, the editor reflects on the current political climate in the US and its impact on theatre education. In Issue 2a, the articles focus on educational theatre in the time of COVID-19 and cover the scope of classroom-based educational theatre practice in urban and rural K-12 settings, colleges and universities, and on implementing research-based theatre online. Roxane E. Reynolds describes her experience transitioning to remote instruction in a secondary school in Dallas, Texas. Jessica Harris illuminates the ways in which the digital divide (lack of access to high speed internet to rural areas and/or folks from low-socioeconomic backgrounds) in rural Fluvanna County, Virginia challenges theatre educators to re-think their approach to distanced learning. On the college front, Alexis Jemal, Brennan O'Rourke, Tabatha R. Lopez, and Jenny Hipscher were tasked with devising a theatre in education (TIE) program for a course at CUNY School of Professional Studies in New York. Their liberation-based social work practice required a new approach as they transitioned online. Cletus Moyo and Nkululeko Sibanda document transitioning practical theatre courses to distance learning at Lupane State University in Zimbabwe, echoing the challenges related to equity and access that Harris experienced. Chris Cook, Tetsuro Shigematsu, and George Belliveau at the University of British Columbia in Canada query: What teaching practices endure in the online research-based theatre classroom, and what new ways practices were fostered through their emerging partnership with technology? For the final article in this issue, Saharra L. Dixon, Anna Gundersen, and Mary Holiman take us out of the classroom and into the field with their reflection on creating and presenting The #StayHome Project, a devised ethnodrama. Their article explores theatre's ability to help communities process collective trauma, build resiliency, and facilitate dialogue around politics and what it means to return to a "new normal".
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.032 | 0.029 |
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".