MFN Clauses as Bilateral Commitments to Multilateralism: A Reply to Simon Batifort and J. Benton Heath
Bibliographic record
Abstract
Most-favored-nation (MFN) clauses have been included in international commercial treaties for many centuries. They also figure prominently as standard provisions in almost any international investment agreement (IIA). Their longstanding and widespread use notwithstanding, investment law doctrine and arbitral practice continue to struggle with the clauses’ application and interpretation, in particular as regards their scope of application. What Stanley Hornbeck observed more than one hundred years ago in this Journal, that “there appear[s] constant disagreements and ever-recurring irritation over what is the meaning and what are the obligations attaching to this or that [MFN] clause,” still characterizes the practice of investment tribunals and the literature on MFN clauses in IIAs today.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.041 | 0.038 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".