Bibliographic record
Abstract
Abstract With the benefit of hindsight, much scholarship across political science, law, and economics has told the story of the international trade regime as if it had been pulled all along by a definite aim. By contrast, this article emphasizes the contingent aspects of the trade regime's development, looking especially to its dispute settlement mechanism. The very creation of the Appellate Body had by no means a certain outcome, and once created, the tribunal's evolution was largely unanticipated by states. An often-overlooked actor played a key role in that development: the WTO Secretariat. Drawing on recent findings, this article lays out the full extent of the Secretariat's role in dispute settlement, which remains largely hidden from view, and deliberately so. From appointing adjudicators and managing their remuneration, to providing them with legal arguments and drafting final rulings, the Secretariat of the WTO looms larger than in any comparable tribunal. Making its influence more transparent, I argue, would go a long way to returning the system to the shape it was designed to have at its outset.
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 | 0.041 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".