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

2023· other· en· W7034135098 sur OpenAlexaff

Notice bibliographique

RevueResearch Portal (Queen's University Belfast) · 2023
Typeother
Langueen
DomaineSocial Sciences
ThématiqueMinority Rights and Languages
Établissements canadiensQueen's University
Organismes subventionnairesEngineering and Physical Sciences Research Council
Mots-clésMetaverseNature versus nurtureVirtual realitySet (abstract data type)The InternetSpace (punctuation)Virtual worldWork (physics)Domain (mathematical analysis)Emerging technologiesGame Developer
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,018
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,154
Score d'incertitude au seuil0,306

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,018
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0080,009
Communication savante0,0190,015
Science ouverte0,0020,015
Intégrité de la recherche0,0180,007
Charge utile insuffisante (le modèle a refusé de juger)0,0280,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,054
Tête enseignante GPT0,343
Écart entre enseignants0,290 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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