XV: The eXtended meta-uni-omni-Verse: Introduction, Taxonomy, and State-of-the-Art
Bibliographic record
Abstract
The term "Virtual Reality'' (VR) has been in use since 1938, but the recent barrage of jargon, confusing new terminology, and the hype around VR and a plethora of related realities (AR, MR, etc.) have made it necessary to bring some order to the "Realities''; in fact, it has been largely recognized that there are a lot of "grey'' areas between the Realities, making it desirable to embrace an overarching vision. Throughout the world, many have chosen to use the framework of eXtended Reality (XR) as this unifying overarching concept to interpolate between the "Realities'' and to eXtrapolate beyond them. Together with XR, there is also XI (eXtended Intelligence) for which the IEEE has convened the Council on eXtended Inteligence, or CXI. A related concept is the metaverse, i.e. shared VR, introduced in 1974 as "Metavision'' and "Metaveillance'' and recently popularized by Facebook. In this paper we propose XV as an overarching term, concept, and taxonomy for shared (social) XR across all of the "Verses'', including the universe (physical reality, i.e. "atoms''), the metaverse (virtuality, i.e. "bits''), the omniverse and multiverse, etc. We also briefly outline the state-of-the-art in the various realties and verses covered by XV.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".