US AND CANADIAN LAW ON COMPULSORY LICENSING OF INTELLECTUAL PROPERTY RIGHTS
Bibliographic record
Abstract
Relevance. The relevance of scientific research is that compulsory licensing is a mechanism that allows the state or third parties to use intellectual property (IP) objects without the consent of the copyright holder, but with compensation. This tool is used to balance the interests of copyright holders and society, especially in cases where IP monopoly may impede access to important technologies, medicines or cultural goods. This article discusses the specifics of US and Canadian compulsory licensing legislation, as well as their practical application. Purpose of the article. The purpose of the article is to study the concept, content and nature of the institution of compulsory licensing using the example of the legislation and judicial practice of the USA and Canada. The convergence of US and Canadian approaches to compulsory licensing may facilitate more efficient use of this mechanism. Methods. The leading method of researching the problem was the deductive method, which made it possible to study the legal nature of the institution of compulsory licensing. The article uses inductive method, method of system scientific analysis, comparative legal and historical methods. The leading method underlying the solution to the problem is the comparative legal study of demonstrative court cases on compulsory licensing. Results. The article concluded that Canada's enforcement licensing jurisprudence demonstrates the importance of this tool in protecting the public interest, particularly in the area of access to medicines. Examples of court decisions such as Apotex Inc. v. Merck & Co. and Eli Lilly and Co. v. Canada illustrate how compulsory licensing can be used to provide access to important technologies and medicines. However, its application comes with certain challenges, such as legal barriers and economic consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".