Bibliographic record
Abstract
This presentation is about the relationship between the factors of space, story and body in non-fictional VR projects with the user as a first-person protagonist. What used to be called immersive journalism, i.e., the attempt to offer the user physical experiences of factual journalism, has now become much more film-oriented in the last years. Most projects, therefore, tell stories that have a relation to reality and allow the protagonists to enter real-life narratives. The paper starts by focusing on the state of the art of research from the direction of media and information science. Mel Slater has been investigating VR projects with others since 2005, naming factors such as place illusion or plausibility illusion; in later writings, he expanded this to include the body-oriented sense of embodiment (SoE) (Slater et al. 2009, 2010a, 2010b, 2012). These methodological explanations are supplemented by the immersion effect of the story itself. Domenic Arsénault pointed out there are three levels of narrative immersion in VR (Arsénault 2005). From this, an integrative matrix is developed. In the following, four current examples are presented that were used for the research project. The results of a small, non-representative study with 24 participants are presented afterwards. The findings include, among other things, that both the space and the story in first-person VR are related to the user's body, while the installation of a working plausibility illusion is rather insignificant. The user reads, experiences and understands the projects with the senses, perceptions and cognition of the whole body, as it seems, which makes it possible to speak of an epistemic of the body.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".