Canada’s Current Position with Respect to Sound Marks Registration: A Need for Change?
Bibliographic record
Abstract
This paper analyses and criticizes Canada’s position on sound marks registration in order to recommend the ways in which Canadian policy-makers could further act in order to advance this area of law. The first part of this paper exposes the fundamental concepts of trade-marks as they are necessary to the comprehension of the problems surrounding the registration of sound marks. In the second part, legal considerations associated with the registration of sound marks are discussed. More specifically, the visual requirement, the issue of “use,” the concept of distinctiveness and the question of overlap with copyright are assessed. In the third part, practical concerns related to the representation of sound marks are addressed and recommendations are made.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.027 | 0.020 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 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".