The Importation of the Fair and Equitable Treatment Standard Through MFN Clauses: An Empirical Study of Bits
Bibliographic record
Abstract
The fair and equitable treatment (FET) standard is now found in the vast majority of investment treaties. This article examines the following question. In the event that a BIT does not include an FET clause, can the investor be allowed to invoke the most-favoured nation (MFN) clause, which is typically contained in such treaties, to claim the benefit of an FET clause found in another treaty entered into by the host State? My review of all investment arbitration cases dealing with this issue will show that all tribunals have so far accepted the importation of FET protection through MFN clauses. This article contains the first comprehensive empirical analysis of all those MFN clauses contained in BITs that do not include an FET clause to determine whether or not their scope allows for the importation of FET protection. Finally, one question addressed in this article is whether such an importation should be allowed in all situations or whether there should be circumstances where it should not be permitted.
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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 | 0.012 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".