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Record W7034135098

Shaping the Metaverse: policy engagement with immersive technologies in the UK

2023· other· en· W7034135098 on OpenAlexaff

Bibliographic record

VenueResearch Portal (Queen's University Belfast) · 2023
Typeother
Languageen
FieldSocial Sciences
TopicMinority Rights and Languages
Canadian institutionsQueen's University
FundersEngineering and Physical Sciences Research Council
KeywordsMetaverseNature versus nurtureVirtual realitySet (abstract data type)The InternetSpace (punctuation)Virtual worldWork (physics)Domain (mathematical analysis)Emerging technologiesGame Developer
DOInot available

Abstract

fetched live from OpenAlex

The Metaverse refers to a future evolution of the Internet where the physical and cyber domains achieve convergence. It will emerge over the next decade via a set of nascent networking and computing technologies that will immerse users in realistic or imagined 3D virtual worlds that are dynamically rendered in real-time. The environments will provide ‘presence’ i.e. a sense of collective reality brought about by a simulated world that can be viewed and interacted with by multiple users simultaneously. Proto-metaverse environments such as the massive multiplayer online games Fortnite and Roblox, are bringing forward cultural and social change. To some players, virtual objects have just as much value as physical assets and virtual identities are just as valid as their ‘real’ selves. Metaverse is viewed as a massively creative space that facilitates experimentation and development of ideas in the digital domain that can then transcend into physical goods and services. Music, fashion and the design sectors are likely beneficiaries of an emerging ‘builder economy’ in the metaverse. Further work is needed to nurture this new sector via strengthened IP protection, development of ethical guidelines and tools/registries of creative outputs. The UK is home to the largest games industry in Europe. Games developers will have significant impact not only on the development of virtual worlds for the Metaverse, but also on more industry focussed mixed-reality applications such as digital twins in the architecture, engineering and construction sectors. Game engines are a prime enabling technology for the Metaverse and skills development programmes in the games sector are vitally important to enable the UK to exploit metaverse opportunities in multiple commercial sectors. British cultural landmarks, institutions and assets should be leveraged to bootstrap a uniquely ‘British Metaverse’. Forming a collaborative triple-helix between government, industry and academia would provide a Metaverse anchor point for cultural tourism to the UK and a showcase for the wealth of indigenous, creative talent in the music, fashion and design sectors. The Metaverse products and digital artifacts produced by UK companies needs to be portable between different virtual worlds and the associated platforms that host them. There are insufficient technical standards to enable that portability and ensure a level playing field for small enterprises. International standardisation is the critical path towards interoperability and the UK should take an active and strategically informed approach to participation in Metaverse standards fora. Schemes that incentivise participants, from UK based SME’s and academia, to engage with standards bodies should be considered. Privacy, data collection and the use of biometrics in the Metaverse are pressing concerns. A lack of effective social science investigation into existing social network platforms means that predictions about the long-term effects of Metaverse exposure and its potential for causing harm, are not defensible. EU and US law makers are moving towards regulated data access for qualified researchers to very large online platforms. A joint statement following a US-EU Trade and Technology Council meeting held in May 2023 said, “It is crucially important for independent research teams to be able to investigate, analyze and report on how online platforms operate and how they affect individuals and society”. This approach could open the door to a new era of ‘computational social science’: “Science rarely proceeds beyond what scientists can observe and measure, and sometimes what can be observed proceeds far ahead of scientific understanding. The twenty-first century offers such a moment in the study of human societies. A vastly larger share of behaviours is observed today than would have been imaginable at the close of the twentieth century. Our interpersonal communication, our movements and many of our everyday actions, are all potentially accessible for scientific research.” (Lazer et al., 2021). A similar legislative program in the UK would provide the means to study Metaverse harms, model their epidemiology, predict their consequences and develop countermeasures for immersive virtual worlds that protects the mental and material well being of our young people into the future.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.154
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0080.009
Scholarly communication0.0190.015
Open science0.0020.015
Research integrity0.0180.007
Insufficient payload (model declined to judge)0.0280.003

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.

Opus teacher head0.054
GPT teacher head0.343
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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