Borrowing from our Common Law Cousins: American and British Influences on the Merger of Canadian Trademark and Internet Domain Name Laws
Bibliographic record
Abstract
This article posits that Canadian trademark law is sufficiently circumscribed to regulate the realm of Internet domain names. The overarching purpose of its thesis aims to identify an inherent compatibility between Canadian trademark law and the Internet, while also clearly delineating the Canadian legal framework vis-a-vis domain names. In shedding light on this legal symbiosis, the article ventures upon a comparative study of Canadian, US, and UK jurisprudence, while also taking stock of certain arbitral structures such as ICANN, in order to highlight horizontal legal transplants that will have direct incidence on Canadian online business. Among the themes canvassed, particular emphasis is placed on the ever-increasing extraterritoriality of US law in this field, along with the acknowledgment that US judicial precedents wield considerable influence over Canadian intellectual property policy. As a corollary to this proposition, deference to transboundary domain name litigation and transnational law-derived considerations also pervade the discussion. The merging of Canadian trademark law and Internet domain name regulation is ultimately actuated through the extension of foundational trademark concepts, such as confusion and passing off, to the Internet, along with judicial pronouncements emanating from cognate common law jurisdictions, which are conflated into a single, overriding approach.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.025 | 0.023 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".