Global food security and market stability: The role and concerns of large net food importers and exporters
Bibliographic record
Abstract
During the last two decades agricultural trade has increased substantially. One consequence of this is that almost 20 percent of all calories consumed worldwide are provided by traded food. A number of emerging economies and newly developed countries are now main actors in world trade. Some countries like China, Korea and Saudi Arabia have become large net importers as a consequence of the rapid increase of consumption resulting from economic growth and a growing middle class. Others like Brazil, Argentina and Thailand have modernized their agricultures, improved the use of their ample natural resources, increased exponentially their production and are now main net exporters. The end result of these processes is that five countries (China, Korea, Japan, Russia and Saudi Arabia) are responsible for about 40% of food net imports and seven countries (Argentina, Australia, Brazil, Canada, New Zealand, Thailand and USA) account for about 55% of total food net exports. The impact of these main players on the international market stability and prices is enormous. In the context of the present difficulties to progress in multilateral trade negotiations, it is suggested that a special group composed by major food net importing and exporting countries should be formed within the WTO to promote dialogue, exchange of information and possible agreements and commitments between themselves. It is argued that it would contribute to global market stability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".