Emerging Powers, Leadership, and <scp>South–South</scp> Solidarity: The Battle Over Special and Differential Treatment at the <scp>WTO</scp>
Bibliographic record
Abstract
Abstract Emerging powers, such as China and India, have used claims of developing world leadership and South–South solidarity to strengthen their bargaining position in WTO negotiations. Yet analysis of the growing battle over special and differential treatment (SDT) suggests that such claims are increasingly tenuous. The question of how emerging economic powers should be classified and treated under global trade rules has become an acute source of conflict in the trade regime. The emerging powers insist on access to SDT as an unconditional right of developing countries. But in a debate dominated by the emerging and established powers, the interests of most developing countries have been largely overlooked. Drawing on the cases of agriculture and fisheries – two areas of international trade of particular importance to the developing world – I show that extending SDT to the emerging powers is increasingly problematic for global development. In these areas, many emerging economies are now among the world's largest subsidizers, and the harmful effects of their policies are felt most keenly by other developing countries. Granting SDT to exempt emerging subsidizers from WTO disciplines would therefore undermine efforts to use global trade rules to promote global development, as well as to protect the environment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".