Bibliographic record
Abstract
Abstract Despite a general push for greater transparency, opacity continues to play an important function in international tribunals. The World Trade Organization (WTO) is a case in point. While it has done much to increase its openness, the very design of its dispute settlement body is premised on anonymity in some essential respects. We examine two such instances, each dealing with the authorship of dispute rulings. First, we use text analysis tools to demonstrate that the WTO’s panel reports appear to be largely drafted by WTO Secretariat staff rather than the panellists themselves. This appears especially true for the WTO’s most systemically important disputes. Second, we show that the formal anonymity of dissenting opinions, which is required by the WTO’s rules, is a thin veil. Using the most recent Appellate Body’s dissent for demonstration, we use text analysis to pinpoint its likely author. In both these instances, we argue that anonymity exists by design: it serves to strike a balance between judicial autonomy and political control. Yet, in both settings, due to increased scrutiny and widespread access to text analysis tools, the equilibrium relying on anonymity is likely to be upset, with implications for the institution’s future design. We argue that the ultimate result may be a beneficial one and offer a menu of reform options.
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 | 0.040 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| 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".