Expanding the magic circle: Immersive storytelling that trains environmental perception
Bibliographic record
Abstract
Scholarship on immersion in simulated environments often emphasizes cognitive immersion, or the suspension of disbelief that takes place in an illusionistic space that simulates reality, making the fact of mediation disappear in the experience. Marie-Laure Ryan writes that: “immersivity can be understood in two ways: in a properly VR sense, as the technology-induced experience of being surrounded by data, and in a narrative sense... as being imaginatively captivated by a storyworld” (230). Both of these definitions rest on the notion of cognitive immersion. Grounded in the field of post-dramatic multimedia performance, this paper will focus instead on immersive storytelling that activates the senses in a phenomenological experience. Rather than transporting the spectator into a fictional imaginary space, post-dramatic multimedia performance aims to make participants aware of their presence in the here and now (Klich and Scheer, 128). This paper will describe an immersive storytelling project that integrates virtual reality (VR) into live participatory performance events that take place outdoors. The paper is co-authored by an artist-researcher and two students who are working as research assistants on this project. We recount our creative research process in developing a pervasive game, which Montola defines as a “game that has one or more salient features that expand the contractual magic circle of play socially, spatially or temporally” (2005, 3). This game is played in a park and at key moments, inside VR environments that simulate that same park. The purpose of the game is to attune participants to the species in that particular environment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".