Into the Metaverse: Technical Challenges, Social Problems, Utopian Visions, and Policy Principles
Bibliographic record
Abstract
The metaverse holds a prominent place in debates over the future direction of digital networks. Proponents claim that advances in virtual and augmented reality will shape every facet of social life. This article defines the metaverse, explores the state of the technology, and addresses its public policy significance. It makes use of a political economic perspective focusing on the concepts of commodification and spatialisation. Specifically, it considers how major platform and gaming companies plan to use the metaverse to expand market share. The article also addresses the cultural dimensions of the metaverse as the latest in a series of utopian visions of a digital sublime. It proceeds to take up the social problems associated with the metaverse and concludes by describing the essential policy principles that should guide public authorities in the regulating the metaverse. These principles include acknowledging that current concerns over implementation do not limit future deployment. Moreover, public policy should start by recognising that the metaverse is a public space and not the private property of the major platforms. Finally, policy must address specific social problems deepened by the arrival of the metaverse including crime, privacy, the impact on climate, and data ownership.
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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 | 0.032 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.090 |
| Scholarly communication | 0.042 | 0.061 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 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".