A Quantifiable Framework for Describing Immersion
Bibliographic record
Abstract
Abstract Current definitions of immersion describe its relationship to presence and allow for relative comparisons between the immersive qualities of Virtual Reality (VR) systems, but lack the ability to describe the immersion supported by a system as an absolute quantity. In this article, we present an abstract model of perception, defining sensory units as the smallest biological registers of sensation within the body. Two metrics of immersion are introduced: the immersed sensory range, and the immersed sensory field, which can be defined for both individual sensory units and entire sensory categories. We define an isolated sensory unit as one that is shielded from non-VR stimuli, and derive the terms isolated field and isolated range from this definition. These metrics are further described as ratios, resulting in a set of theoretical and practical attributes which can be used to quantify the immersive potential of a VR experience.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".