Global Power Shifts and the Cotton Subsidy Problem: How Emerging Powers Became the New Kings of Cotton Subsidies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 As this article shows, however, the prevailing view of the global cotton subsidy problem is now simply outdated and inaccurate.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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it