Can Export Restrictions be Disciplined Through the World Trade Organisation?
Bibliographic record
Abstract
Abstract A number of major agricultural exporting countries responded to high food prices from 2007 to 2011 by imposing export restrictions on agricultural commodities in efforts to constrain domestic food price inflation. These restrictions reduced the volume of internationally traded food and exacerbated international price spikes. Net food‐importing countries were faced with growing import bills, and non‐governmental organisations that target food security scaled‐back programme commitments and appealed for increased funding. There have subsequently been a chorus of calls for the development of a formal international framework that could discipline the use of agricultural export restrictions; the agreements of the World Trade Organisation (WTO) have been targeted as possible fora for such disciplines. We present a framework in which the efficacy of such disciplines can be analysed and conclude that constraints on agricultural export restrictions are not likely to be effective within the WTO's Dispute Settlement Understanding for two reasons. First, the timelines for dispute settlement in the WTO are too long to be useful in disputes about export restrictions during periods of high food prices. Second, the withdrawal of tariff concessions, or trade retaliation, that could be authorised in such cases would not be a credible response for many complainant countries.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".