Bibliographic record
Abstract
(A project of the Digital Dramaturgy Lab at the Centre for Drama, Theatre and Performance Studies, University of Toronto) This article discusses the 2017 festival-based undergraduate course, “Theatre Criticism and Festival Dramaturgy in the Digital Age in the Context of Globalization—A Cultural-Comparative Approach” as a platform for experiential learning. The course, hosted by the University of Toronto’s Centre for Drama, Theatre and Performance Studies, and based on principles of our Digital Dramaturgy Lab, invited a small group of undergraduate students to critically investigate two festivals—the Toronto Fringe Festival and the Festival d’Avignon—in order to engage as festival observers in criticism and analysis of both individual performances and festival programming/event dramaturgy. We argue that site-specific modes of experiential learning employed in such a project can contribute in meaningful ways to, and expand, current discourses on festivalising/festivalization and eventification through undergraduate research. We focus on three modes of experiential learning: nomadic learning (learning on the move, digital mobility), embodied knowledge (learning through participation, experience, and feeling), and critical making (learning through a combination of critical thinking and physical making). The article begins with a brief practical and theoretical background to the course. It then examines historical conceptions of experiential learning in the performing arts, including theoriesadvanced by Burnet Hobgood, David Kolb and Ronald Fry, and Nancy Kindelan. The importance of the festival site is then discussed, followed by an examination of how the festivals supported thethree modes of experiential learning. Samples of student works are used to support this analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".