Caveat Cloudster: Why Traditional Common and Civil Property Law Should Apply to Virtual Property and How It Will Change the Legal Realities of the Internet
Bibliographic record
Abstract
With the increasing trade in and production of virtual content (e-books, digital music, files stored in the cloud, etc.) and an ever growing use of virtual real estate (email accounts, online storefronts, URLs, etc.) in commercial transactions, the legal interest users hold in their virtual property will determine whether all have the power to prosper in this new, multi-billion-dollar virtual economy. In the cloud, service providers can grant or destroy scores of virtual property with the click of a button and without compensation—a power not even available to the Canadian government. As it stands, the legal regime governing virtual property is economically and socially unviable. The extension of traditional property law principles to new types of virtual property would better protect the reasonable expectations of parties involved in these electronic transactions. This article defines virtual property and its legally relevant characteristics before turning to examine the licensed-but-not-sold contractual regime that governs virtual property today. It argues that virtual property more closely resembles physical property than intellectual property. In addition, it concludes that utilitarianism and personality theory justify the creation of a legal duty for service providers to protect the interests that users maintain in their virtual property. Lastly, the author suggests how the creation of property rights in virtual property might come about and offers a starting point for future debate as to the nature of the rights that ought to be recognized.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.021 | 0.039 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".