Bibliographic record
Abstract
For a decade now, multiplayer virtual worlds have played a prominent social and economic role in society. Users of virtual worlds are vast in number, and they exchange—often against real-world currency—virtual objects (avatars, virtual clothing, virtual homes, etc.) created or purchased in virtual worlds and over which they claim ownership. However, this virtual property currently has no legal status under domestic or international law. The developers of virtual worlds exploit this regulatory vacuum and apply the legal framework that best serves their interests to the end-user licence agreement (EULA) that every user must accept before entering the virtual world. Virtual goods by definition do not physically exist; they appear as images only in the virtual worlds that host them. If we accept the traditional view of property, virtual property cannot find a legitimate place in the property law of France and Quebec. This is in contrast to common law, which has a much broader stance on the notion of property. In the end, questions regarding virtual property are best examined within the framework of intellectual property law, a branch of property law tailored to intangible objects: intellectual property rights were established specifically to regulate ownership of intangible goods and are neither diminished nor limited by an object’s lack of existence—physical possession is thus immaterial.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".