(De)Legitimation at the WTO Dispute Settlement Mechanism
Bibliographic record
Abstract
Similar to many types of legal institutions, international courts employ a wide variety of legitimation strategies in order to establish and maintain a sound basis of support among their constituents. Existing studies on the legitimacy or legitimating efforts of the World Trade Organization's (WTO) judicial bodies have relied largely on theoretical or normative priors about what makes these institutions legitimate. In contrast, this paper directly connects the study of courts' legitimating efforts with their effects by empirically mapping how the WTO Dispute Settlement Mechanism's (DSM) exercise of authority has been received by the system's primary constituents—WTO Members. Drawing on an original dataset of statements made by WTO Members within meetings of the Dispute Settlement Body from 1995-2013 and a series of interviews, this paper provides a descriptive analysis of expressed views on its exercise of authority over time and across subsets of Members. In addition, through an in-depth examination of statements on focal reports, this paper sheds new light on the sources of the DSM's legitimacy by identifying practices that contribute to reducing or enhancing it in the eyes of the primary constituents of this international institution.
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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 | 0.037 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".