Bibliographic record
Abstract
Professors Shaffer and Gao, in their thoroughly researched article, challenge an assumption common in the early years of the new World Trade Organization (“WTO”) dispute settlement system: that the legalization of dispute settlement in the transformation of the General Agreement of Tariffs and Trade into the WTO would disadvantage states without strong traditions of domestic or international adversarial litigation. This was of particular concern because the legalization appeared to benefit, for example, the United States, the European Union, and Canada—all countries with considerable resources in international economic law and litigation, including resources “in house” within the government. As Shaffer and Gao show in their article through the example of China, it is entirely possible to play catch-up and evolve in capacity but also through an effective, winning strategy in WTO litigation. In a different study (with a different co-author) Shaffer has told a similar story concerning Brazil.
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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.081 | 0.080 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".