The Metaverse and Beyond: Implementing Advanced Multiverse Realms With Smart Wearables
Bibliographic record
Abstract
With the online-everything transformation accelerated by a global Covid-19 pandemic, we may finally find ourselves on the verge of the next potentially paradigm-shifting step after the mobile Internet: The Metaverse. Among others, the Metaverse will utilize head-mounted devices (HMDs) and extended reality (XR), including but not limited to virtual and augmented reality (VR/AR), as the medium to connect avatars and users in the real world. In addition, the Metaverse is supposed to provide gamified experiences around emerging Web 3.0 technologies and is anticipated to be the precursor of the so-called Multiverse, which will serve as an architecture of advanced XR experience realms. In this paper, we focus on the anticipated 6G post-smartphone era, where smart wearables such as VR/AR HMDs are increasingly replacing the functionalities of smartphones. Our contributions are threefold: ($i$) we first extend Metaverse’s primary focus on VR/AR to Multiverse’s advanced XR realms of experience. Next, we gamify and implement all eight Multiverse realms of experience using Oculus Quest 2 and Microsoft HoloLens 2 as state-of-the-art VR/AR HMDs, experimentally investigating and comparing the performance of a ($ii$) single-player origami game and ($iii$) multi-player maze game across our proposed integrated VR/AR HMD and Amazon Mechanical Turk crowd-of-Oz (CoZ) platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".