Bibliographic record
Abstract
This article proposes a comparative analysis of the practice of adjudicators in the areas of international trade law and international investment law in an attempt to detect commonalities and differences with respect to the method of identifying “the ordinary meaning to be given to the terms of the treaty.” It traces how these adjudicators have understood the concept of ordinary meaning and where they have looked for the “ordinary meaning of the terms of the treaty.” The article also touches upon the question of the timing of the interpreters’ determination of ordinary meaning, and contrasts the concept of “ordinary meaning” to that of “special meaning” in their practice. The comparative analysis allows for the identification, at a very generalized level, of at least two broad trends. First, adjudicators often have the immediate reflex of consulting dictionaries as the starting point of the determination of “ordinary meaning.” Second, regardless of whether these dictionaries have provided them with a satisfactory meaning or not, adjudicators almost immediately proceed to contextualizing this meaning, either as part of their continued quest for the “ordinary meaning” of the interpreted terms, or as part of the next steps in the interpretative process.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.084 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".