The Emergence of Metaverse Technologies and Their Implementation Across Various Industries
Bibliographic record
Abstract
The metaverse is the latest buzzword from the technology domain as it promises to transform the ways in which people work, learn, shop, socialize, and entertain themselves. The announcement made by Meta founder Mark Zuckerberg to focus on developing the metaverse has given fresh momentum to a phenomenon that received relatively little attention during the past three decades. Various technologies that support the Metaverse have already been developed, while others are currently in the development stage, for example, virtual reality, augmented reality, the Internet of Things, blockchain, and big data. However, this scoping review aims to present a comprehensive overview of the current state of research on the metaverse, including the degree to which researchersand industry expertsarefocusingonitsmultipleaspects, notonly thetechnology and infrastructure but the economic and social factors as well. A total of eight themes have been identified as a result of the scoping review and thematic analysis. This report discusses these themes individually and in relation to one another to determine the general focus of research on this topic and identify areas where more attention needs to be paid. It is expected that by considering the recommendations presented in this report, the industry will be able to achieve balanced and sustained growth of the metaverse while avoiding some of the pitfalls that have been experienced in earlier decades due to misplaced enthusiasm and exaggerated claims about the potential of disruptive and novel technologies.
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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 | 0.046 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".