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Record W3007378934 · doi:10.5539/jpl.v13n1p75

Virtual Reality: General Issues of Legal Regulation

2020· article· en· W3007378934 on OpenAlexvenueno aff
Роман Дремлюга, Olga Dremliuga, Andrei Iakovenko

Bibliographic record

VenueJournal of Politics and Law · 2020
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
FundersFar Eastern Federal University
KeywordsCyberspaceVirtual realityEntertainmentPerspective (graphical)Field (mathematics)Emerging technologiesComputer scienceEngineering ethicsThe InternetHuman–computer interactionPolitical scienceEngineeringLawWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

The article focuses on the general issues of legal regulation of relations that emerge in the field of application of VR technologies and presents issues associated with the regulation of development of such technologies. It looks at the features of this technology that create challenges for the development of a system of legal regulation of its application. The article also gives a perspective at major factors that make application of the existing law difficult and offers analysis of the emerging issues of its regulation. The author arrives at a conclusion that this technology is fundamentally different from the other existing technologies as it combines the properties of both physical reality and cyberspace. Among the challenges of the legal regulation of VR are a high realism, complete immersion user experience, and low cyber protection of both hardware and software components. The author evaluates several regulatory approaches, which could be used in the case of virtual reality and finds that all of them have major deficiencies. Contemporary research findings in secure application of VR in the fields of teaching and entertainment get rapidly outdated as they cannot catch up with the technology development, therefore they can only serve as a ground for the development of a system of VR regulation with consideration of this factor.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.306
Teacher spread0.268 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations12
Published2020
Admission routes1
Has abstractyes

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