EQUUSTEK V GOOGLE: LA RHÉTORIQUE DE LA VIRTUALITÉ EN DROIT INTERNATIONAL PRIVÉ
Bibliographic record
Abstract
This paper examines the case of Equustek v. Google, currently pending before the Supreme Court of Canada, and proposes a novel approach to the cyberspace debate in private international law. The author categorizes the rhetoric of the debate under two different themes: virtuality and internationalism. The virtuality rhetoric calls for the adaptation of traditional territorial connections as a result of their incompatibility with cyberspace. The rhetoric of internationalism calls instead for the adaptation of traditional territorial connections by highlighting the virtues of globalization and innovation. The author argues that the reform of Canadian private international law in the 1990s corresponds to the nature of the legal challenges associated with the democratization of a “borderless” cyberspace. Thus, cyberspace must be considered in light of the rules of private international law as shaped by internationalist rhetoric. This analysis provides insight into the forces at work in the Google case and the positions and attitudes underlying them. It also facilitates an adequate assessment of the legal ramifications of cyberspace in private international law. The author concludes by arguing that political and social considerations shape the development of conflict rules much more than the technical nature of any given medium. Conflict rules are not developed in the abstract and nor should the corresponding legal discourse.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".