The Pedagogy of Grief: Lessons from Making Zoom Theatre During a Pandemic
Bibliographic record
Abstract
While the closure of theatres across Canada and around the world when the pandemic hit in March 2020 caused widespread feelings of loss amongst theatre practitioners and educators, the institutional pressures to find ways to survive and move on prevented many from processing their grief. This article examines The Stream You Step In (TSYSI), a series of four original Zoom plays co-produced by Outside the March and the University of Windsor in 2020, through the intersecting perspectives of pedagogy and grief. TSYSI was a response to grief on different levels: graduating students’ grief for their mainstage roles, which they had been working towards since entering the program but which they lost when the pandemic forced the cancellation of their planned theatre season; theatremakers, educators, and audiences’ collective grief for the loss of live theatre and the community it creates; and the grief experienced by society at large for the loss of Black and Indigenous lives, magnified in summer 2020 by the global outrage in response to the death of George Floyd and many others. Through reflection on two TSYSI plays, Karen Hines’s The River of Forgetfulness and David Yee’s good white men, this article stresses the importance of making space for grief within pedagogy. It argues that a grief-led creative process can honour what was lost, create and strengthen community, and foster opportunities to make new meaning. As theatres and educational and training institutions continue to face uncertainty, this article’s provocations contribute to ongoing conversations around ethics, representation, and collaboration in Zoom theatre.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| 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".