Global Power Shifts and the Cotton Subsidy Problem: How Emerging Powers Became the New Kings of Cotton Subsidies
Bibliographic record
Abstract
Abstract Cotton is one of the most contentious issues in the global trading system. Subsidies provided by richer countries have had a devastating impact on the welfare of poor cotton farmers in the developing world. Cotton subsidies have long been seen as a symbol of the injustices of the trading system—a harm perpetrated by the rich countries of the Global North against the poor countries of the Global South. This conception of the global cotton subsidy problem is deeply entrenched and has profoundly shaped contemporary debates about power and fairness in the multilateral trading system. As this article shows, however, the prevailing view of the global cotton subsidy problem is now simply outdated and inaccurate. Today, the biggest providers of cotton subsidies are no longer the United States and EU but emerging economic powers like China and India. These major developing countries are providing large volume of subsidies, which are distorting global production and trade, and harming some of the world’s poorest and most vulnerable farmers in other developing countries. The cotton subsidy problem is no longer simply a North–South issue. Addressing the problem requires tackling all harmful subsidies, including those from large emerging economies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".