Immersive Storytelling in Creative Media, Smart Manufacturing and Healthcare
Bibliographic record
Abstract
The paper explores interactive storytelling in Augmented and Virtual Reality. It is based on the project ANONYMIZED that bridges the gap between technological developments and their application and aims to combine art, economy and science. Five use cases employ an interdisciplinary research design in the areas of Creative Media, Smart Manufacturing and Healthcare. They all develop prototypical applications but also attend to their contextualization with focus on narrative elements: Each use case defines a ”story” in its own context, the purpose of its narration, and its media specificity. Due to their increased availability, virtual and immersive technologies have not only led to a resurgence of their popularity but also to intriguing new perspectives regarding their use in different contexts. Our research project employs state-of-the art AR and VR technology in five use cases about VR Interaction, Motion Tracking in Artistic VR, Auditory AR for Industry, AR Interfaces for Industry and AR Education for Patients. All of them inquire prototypical applications but relate to the central concept of interactive storytelling. The project addresses three distinct dimensions of storytelling in each use case: the production of a coherent story, the collection and transfer of knowledge, and the specificity of the used technology. Those dimensions are widely known and employed. However, they are seldomly intertwined in one interdisciplinary project focusing on immersive media. Furthermore, the notions of interactivity and immersion play significant roles in the study of storytelling in conjunction with VR an AR. Following papers about immersive storytelling are significant: John Bucher (2017) e. g. examines the timeless principles of storytelling and how they are applied, transformed, and transcended in VR. Marie-Laure Ryan (2015) combines the concepts of immersion and interactive storytelling into a Poetics of Immersion including spatial, temporal and emotional categories. She highlights underlying interactive structures such as the Complete Graph, the Network, the Flowchart. Moya Baldry (2018) discusses the role of the author in complex narratives of immersive environments. Covering the areas of Creative Media, Smart Manufacturing and Healthcare leads to a very specific research design, as each area is connected to different research contexts. Therefore, the paper focuses on the above-mentioned dimensions of storytelling and addresses the following questions: How is the “story” defined in each context and what is its purpose? Which methods and technologies are used to convey it? Which narrative elements show potential and how can they be developed further for future applications? The paper reflects on the outcome of the use cases and potential future applications of interactive storytelling in immersive media.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".