The stunted development of unfair competition law in the United States and Canada
Bibliographic record
Abstract
Abstract Both the United States and Canada present unfair competition law in a way that is complex and indicative of their kindred beginnings. Sharing a closely paralleled history in the development of unfair competition law, these countries exhibit unique similarities in both substance and approach, likely not found in any other jurisdiction. Born out of English common law, the early trajectory of unfair competition was inextricably linked to trade mark law. Both countries’ legislatures passed ambitious trade mark statutes that created federal regulation of certain areas of unfair competition, while also reserving large areas for the state or provincial legislatures to regulate. Claimants therefore navigate substantial variety in unfair competition protections depending on the cause of action. Even so, obligations under international agreements such as the Paris Convention and interaction with other bodies of law further extend the unfair competition legal landscape. Despite its complexity, the United States and Canada share remarkably similar paths to unfair competition protection. Understanding their history, limited national legislative powers, policy rationales, obligations under international agreements and the interplay between federal and state or provincial law create a rich and multifaceted unfair competition landscape.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 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".