Bibliographic record
Abstract
What shapes jurisprudence in international law? States dedicate considerable effort trying to influence not only the outcome, but also the content, of legal rulings. The stakes are high, as these legal opinions can redefine the meaning of the rules. Looking at the World Trade Organization, we ask whether some countries hold more influence over jurisprudence than others, and what such influence depends on. Using text analyses of every country submission in every ruling in the WTO era, we test a number of theoretical expectations. We find that some countries do appear to hold greater sway over the content of rulings than others: a country’s wealth, but especially its legal experience, account for much of this variation. Secondly, countries’ influence over the content of the verdict varies according to how novel the legal issue being ruled on is: states have more influence over the content of the ruling, the less precedent judges have to rely on in terms of prior legal decisions. The salience of the case and judges’ legal experience also follow expectations, as both are shown to take away from countries’ influence. Overall, the degree to which countries’ submissions influence the content of rulings appears to vary systematically. Legal capacity affects not only countries’ ability to file disputes, but also their ability to affect the shape of the resulting jurisprudence.
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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".