Bibliographic record
Abstract
The coronavirus pandemic has been detrimental to live theatre everywhere; however, it has forced us to explore the possibilities within the digital landscape as in-person interactions continue to be limited. Virtual reality, or VR for short, has exploded in popularity due to its escapist and endless possibility, but it also demonstrates inherent elements of game design and theatricality in its applications and experiences – so to what extent do we see such elements and how do they apply? Using preliminary studies in video game and immersive theatre philosophy, interviews with professional artists in these fields, and my own experiments on the Oculus headset, we see connections of concepts such as presence, interactivity, and aesthetics being made in VR, as are similar terms found in theoretical discussions of gaming and immersivity. This connection also recognizes the logistical problems like accessibility and ethics, and though conversations are still developing on these subjects, we can consult with each industry to analyze and predict outcomes of different procedures. This intersectionality of technology and the arts is not new; however, it is underutilized – digital theatre presented a way for people to connect during the pandemic, but with VR, we are given the chance to interact more authentically in a shared, online space.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".