Bibliographic record
Abstract
The object of this paper is to provide an analysis of the way in which MFN has been interpreted and applied in the context of GATT and the WTO agreements. In the first part emphasis is placed on MFN under the GATT. Firstly it discusses the inclusion of the MFN principle in the GATT, especially focusing on Article I:1 of the GATT with its exceptions. Secondly, it discusses the relationship between MFN principle and national treatment principle. Thirdly it points out the issues regarding the interpretation on the MFN principle through the analysis of various cases under the WTO. Fourthly it considers the MFN principle located in other covered agreements, such as GATS and TRIPS. Fifthly it discusses the interpretation on GATT Article I:1 though several cases that analyze the requirements of this Article, its exceptions and the core concept of the MFN principle, the determination of like product. In the second part, it discusses the MFN principle under GATS and TRIPS, as it still serves as a core concept in both agreements. In the last part, it provides an assessment of MFN under WTO jurisprudence, with several issues regarding several regional trade area and plurilateral agreements being raised.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".