On analysis of the disagreement between TRIPs Agreement and Section 337 of the US Tariff Act
Bibliographic record
Abstract
Section 337 of the US Tariff Act often claimed the severest trade barrier in the world,which is the legal rule of imposing control over the imported goods infringing American intellectual property rights.It is U.S.International Trade Commission that investigates and rules the accusations of Section 337 violations.In recent years,the number of investigation into Chinese enterprises has soared to the top of the world,amounting to tens of billions dollars with worryingly low percentage of answering and winning lawsuits.It is China's countermeasure to follow European Union and Canada applying the multilateral dispute settlement mechanism to Section 337 to pushing US to modify the section.However,little argued is the relations between TRIPS and Section 337 of US Tariff Act.Quite a few articles about this hold that the section never violates TRIPS.This paper argues that this section not only violates the national treatment principle in TRIPS Article 3,but also disagrees with TRIPs Article 41,based on denouncing the claim that Section 337 goes with TRIPs.The feasibility is analyzed about applying Section 337 to WTO,and consequently some concrete and constructive operation advice is presented.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| 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".