Blurring Our Real and Virtual Worlds: Canadian and Worldwide Legal Issues Arising From MMORPGs
Bibliographic record
Abstract
In recent years, more and more people have become members of virtual online worlds through the promulgation of massively multiplayer online role playing games (MMORPGs). As of December 28 th , 2008, World of Warcraft, a popular MMORPG, reached 11.5 million players – a figure that would make the fictional world of Azeroth more populated than Cuba. 3 It is possible that by 2011, four out of every five people who use the Internet will work or play in a virtual world. 4 With so many players investing time and money into these online games, legal issues have begun to arise that draw close parallels between game rules and real world laws. Issues such as individual rights and character rights as designated by in-game End User License Agreements (EULAs), ownership of in-game property, gold farming and child labour, and criminal prosecution and jurisdiction for in-game crimes. This paper critically examines the close proximity of in-game legal issues to legal issues faced in the real world, and argues that as more people begin to adopt these technologies, the lines between virtual and real will become increasingly more difficult to discern.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.027 | 0.030 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 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".