The <scp>WTO</scp> in Buenos Aires: The outcome and its significance for the future of the multilateral trading system
Bibliographic record
Abstract
Abstract The conclusion of the World Trade Organization's (WTO) Buenos Aires ministerial conference (10–13 December 2017) was immediately celebrated and derided in equal measure. For its supporters, Buenos Aires opened the way toward negotiations in e‐commerce, investment facilitation for development, and measures designed to help micro, small and medium sized enterprises (MSMEs). For its detractors, the meeting underscored the gridlock that continues to blight the WTO’s negotiating function and underlined the organisation's declining credibility as a mechanism for governing global trade. In this paper we provide one of the first full length critical evaluations of the Buenos Aires conference and its outcome. In so doing, we offer answers to three questions. What accounts for such dramatically different assessments of the meeting's outcome? How should the outcome be interpreted? What is its significance for the future of the WTO and the multilateral trading system? We argue that the meeting's outcome was indeed significant. It has consolidated the process of reconfiguring the WTO‘s negotiating function; and it enables members to tackle more effectively a range of pressing economic and social issues as well as to navigate blockers and blockages in the negotiations. However, it also poses challenges for the WTO‘s poorest constituents.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".