The tort of virtual negligence : a proposition to regulate virtual worlds
Bibliographic record
Abstract
Abstract : No well-established proposal exists for the application of negligence law to hold inhabitants of virtual worlds—avatars and their users—liable for their negligent virtual actions. To address this issue, this essay proposes recognizing the tort of virtual negligence. It is defined using a transsystemic and functional approach that combines aspects of common law, civil law, and public law. Diverging from conventional concepts such as “breach of the standard of care” and “fault,” the tort of virtual negligence proposes viewing virtual actions as decisions that, like administrative decisions, must fall “within a range of possible, acceptable outcomes which are defensible in respect of the facts and law.” The foundations of this proposition are that virtual actions are informational constructs and the users’ conscious decisions, that virtual actions are subject to law, and that users entering a virtual world, presuming they are in good faith, should justify virtual actions that appear to fall outside the apparent customs and rules of a virtual world. Virtual negligence is assessed in a two-step analysis inspired by Erving Goffman’s frame analysis. First, virtual actions are “framed” within a virtual world’s customs and rules, and inspiration is drawn from the concepts of “statutory breach” and “statutory fault.” Courts should evaluate the apparent customs and rules of a virtual world and establish a primary framework of conduct outside of which virtual actions are presumed to be unreasonable. Second, users must justify or “reframe” their virtual actions contextually, which is inspired by how the reasonableness of decision-making is evaluated in administrative law. Practical scenarios are discussed to illustrate the possible application of the tort of virtual negligence. While this essay primarily focuses on how to assess the reasonableness of virtual actions, it also addresses the issues of damages, causation, and the jurisdiction of Canadian courts. This discussion provides a thorough and practical framework for the tort of virtual negligence. Finally, the essay concludes by highlighting the potential benefits of the tort of virtual negligence for improving the regulation of virtual worlds, namely that it would provide a flexible tool for assessing the legality of virtual actions and that it would empower users.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".