Bibliographic record
Abstract
Participatory digital theatre is a relatively new medium, at least in the form that emerged largely out of the COVID-19 pandemic. The increased uptake of ultra-low-latency video-conferencing platforms like Zoom offer new opportunities for designers to create interactive shows that incorporate real-time audience feedback. While far from perfect, these platforms offer a starting place for examining what works and what could be improved for online performance platforms. In this article, some reflections and recommendations are made regarding online participatory theatre design. For example, creating a sense of shared space in an online show can be aided through how audiences and performers are represented onscreen. Obscuring the representation of an audience can be used as a technique to create a more isolated, solitary experience, while the use of chats and avatars can create a more communal experience. Altering the length of streaming delays also affects the experience of a show, with shorter delays favouring more interaction and feedback. It is posited that audience avatars may have a meaningful effect on audience engagement, while the display of self-facing cameras for performers and audience members alike may have a negative impact on the experience of audience members, and the engagement of performers with an audience. The future of digital theatre is perhaps best explored by embracing what the medium does uniquely well—create experiences that are not tied directly to a physical space or time, which embrace interactivity, and which can be long-living online.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".