Bibliographic record
Abstract
This research concerns the relationships among space, story, and body in nonfictional Virtual Reality (VR) projects with the user as a first-person protagonist. What was once called immersive journalism (i.e., attempting to offer users physical experiences of factual journalism) has become increasingly film-oriented over the last five years with the further development of head-mounted displays for the consumer market. As such, projects often tell stories that relate to reality and allow protagonists to enter realistic narratives. Mel Slater has investigated VR projects since 2005, analyzing factors such as place illusion (PI) and plausibility illusion (Psi). In later writings, Slater expanded his research to include the body-oriented sense of embodiment (SoE) (Slater 2009; Slater et al. 2010; De La Pena et al. 2010; Kilteni et al. 2012). Since nonfictional VR mostly tells a story in the direction of the users, incorporating them into the story design, the effects of narration also should be considered. Domenic Arsénault (2005) noted three levels of narrative immersion in VR when dealing with narrative projects. Considering this, it is possible to develop a four-level matrix including place, plausibility, body perception, and narration. Starting in 2020, a Graduate student research project at the University of Leipzig was set up using this matrix to analyse four current examples for a small, non-representative study of twenty-four participants. Our findings demonstrate that space and story in first-person VR experiences are very important, while the installation of plausibility and coherence is less significant. The connecting factor of these two levels is one's own body. Users read, experience, and understand projects with their senses, perceptions, and cognition, making a discussion of an episteme of the body possible. Thus, stories from real life are not conveyed indirectly but experienced directly and personally.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".