Measuring the Cost of Privacy: A Look at the Distributional Effects of Private Bargaining
Bibliographic record
Abstract
Transparency is one of the most contested aspects of international organizations. While observers frequently call for greater oversight of policy making, evidence suggests that settlement between states is more likely when negotiations are conducted behind closed doors. The World Trade Organization’s (WTO) legal body provides a useful illustration of these competing perspectives. As in many courts, WTO dispute settlement is designed explicitly to facilitate settlement through private consultations. However, this study argues that the privacy of negotiations creates opportunities for states to strike deals that disadvantage others. Looking at product-level trade flows from all disputes between 1995 and 2011, it finds that private (early) settlements lead to discriminatory trade outcomes – complainant countries gain disproportionately more than the rest of the membership. When the facts of a case are made known through a ruling, these disproportional gains disappear entirely. The article also finds that third-party participation – commonly criticized for making settlement less likely – significantly reduces disparities in post-dispute trade. It then draws parallels to domestic law and concludes with a set of policy prescriptions.
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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 | 0.019 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".