Bibliographic record
Abstract
This paper examines the potential social risks of the metaverse. Previous research has found that video games, including Virtual Reality (VR) ones, can be associated with violence normalization and objectification of women. Moreover, some studies have suggested that gaming platforms even serve as tools for extremist groups to recruit and radicalize vulnerable people. Nonetheless, these risks have not been further studied in the context of the metaverse. This research analyzed relevant cases from different gaming platforms including Meta Horizon Worlds. Moreover, the game VRChat was studied due to the similarities it has with the metaverse in terms of design and features. An evaluation of VRChat's user experience was carried out through an analysis of its most popular reviews. Though no signs of radicalization were found, an important number of these reviews expressed sentiments such as loneliness and depression, which make people more vulnerable to be radicalized. Additionally, a tendency for online harassment and sexual deviance was found in the game since 2018 and remained persistent to date. For further insight into the sexual misconduct findings, an interview was conducted with psychiatrist and sexuality expert, Dr. Zenteno. This research found that the game developers of VRChat have not done enough to address sexual misconduct effectively and protect its most vulnerable users. This research concluded that due to the lack of incentives that game developers have to regulate their platforms themselves for social good, further intervention of all the stakeholders involved is needed. This includes policymakers, parents, legal guardians, and educators.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".