ESRB Warning: Use of Virtual Worlds by Children May Result in Addiction and Blurring of Borders – The Advisable Regulations in Light of Foreseeable Damages
Bibliographic record
Abstract
This article analyzes the possible models for regulating the use of Internet-based virtual worlds by minors. While virtual worlds introduce a unique experience to their users, there is a strong indication that such use, if left unregulated, may cause harm, especially to minors. This article explains that the dangers associated with virtual worlds are different from those created by other types of media. The various phenomena which may be caused due to the use of virtual worlds and the damages likely to be caused by such phenomena, rest on two assumptions: that minors are especially prone to suffer from such dangers, since the exposure of minors to the experiences offered by virtual worlds is not mitigated by factors such as a more developed sense of reality and responsibility, and, that in the use of virtual worlds there is a greater potential to induce such harms when compared to the use of video games or other Internet applications. The methodology underlying this article is based on a comparativecritical review of the existing literature in the fields relevant to this interdisciplinary realm: technology, psychology, philosophy and law. This article concludes that non-legal regulation is insufficient and puts forth several suggestions for legal regulation. The proposed regulation is based on four principles: Awareness – forcing virtual worlds companies to issue a warning of the possible damages similar to the warnings printed on cigarettes packs; Prevention – operating technological measures to identify minor users and tracking their use length; Help – establishing help centers and posting distress buttons in the virtual world; and Liability – imposing tort liability on virtual worlds companies that fail to implement the proposed changes.
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 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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.020 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".