“Cross Treaty Interpretation” en bloc or How CAFTA-DR Tribunals Are Systematically Interpreting the FET Standard Based on NAFTA Case Law
Bibliographic record
Abstract
Abstract This article examines how tribunals set up under the CAFTA-DR have interpreted the fair and equitable treatment (‘FET’) standard under Article 10.5 in the last 15 years. It shows that they have consistently referred to NAFTA case law to define the standard and to interpret the scope and content of the different elements it contains (arbitrary conduct, legitimate expectations, due process). The only exception is regarding denial of justice. This is a fascinating example of “cross treaty interpretation”. I will explain the reasons why CAFTA tribunals have done so and examine whether or not this “cross treaty interpretation” en bloc is legitimate and sound in light of the canons of treaty interpretation.
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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 | 0.049 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".