Guest Editorial Special Issue on Social Studies, Human Factors, and Applications in Metaverse
Bibliographic record
Abstract
The term “metaverse” was first introduced in Neal Stephenson’s 1992 science fiction novel, Snow Crash. It is conceived as the successor to the contemporary Internet, wherein users, represented as avatars, can interact with others or with applications within a three-dimensional (3D) virtual space, which is ubiquitously accessible. Although the metaverse remains a digital construct, establishing a sophisticated virtual societal framework—including a stable economic system—is paramount as users acquire assets and foster communities therein <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> . The implications become profound and potentially unpredictable should any single entity gain dominance over this virtual societal infrastructure. Such anxieties have been vividly portrayed in recent cinematic offerings such as “Ready Player One” and “Free Guy.” In response, blockchain technology emerges as a promising countermeasure. Trailblazing metaverse platforms leveraging blockchain, such as Decentraland, CryptoVoxels, and Sandbox, utilize cryptocurrency and nonfungible tokens (NFTs) to define programmable assets or access privileges <xref ref-type="bibr" rid="ref11" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[11]</xref> . These tokens may facilitate a borderless and frictionless payment layer, ensuring the uniqueness, persistence, and tradability of users’ digital assets <xref ref-type="bibr" rid="ref9" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[9]</xref> . Furthermore, the rise of smart contract-driven decentralized applications (DApps) <xref ref-type="bibr" rid="ref10" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[10]</xref> —spanning decentralized finance (DeFi) to innovative social applications <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</xref> — ushers in an era of transparent, self-regulating digital ecosystems. As a multimedia community predicated upon vast online participation, advancements in blockchain may pave the way for a fair, transparent, and sustainable metaverse <xref ref-type="bibr" rid="ref13" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[13]</xref> .
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".