The G20 and Food Security: a Mismatch in Global Governance?
Bibliographic record
Abstract
Abstract When the G20 took up food security in 2010, many were optimistic that it could bring about positive change by addressing structural problems in commodity markets that were contributing to high and volatile food prices and exacerbating hunger. Its members could tighten the regulation of agricultural commodity futures markets, support multilateral trade rules that would better reflect both importer and exporter needs, end renewable fuel targets that diverted land to biofuels production, and coordinate food reserves. In this article, we argue that although the G20 took on food security as a focus area, it missed an important opportunity and has shown that it is not the most appropriate forum for food security policy. Instead of tackling the structural economic dimensions of food security, the G20 chose to promote smoothing and coping measures within the current global economic framework. By shifting the focus away from structural issues, the G20 has had a chilling effect on policy debates in other global food security forums, especially theUNCommittee on World Food Security (CFS). In addition, the G20 excludes the voices of the least developed countries and civil society, and lacks the expertise and capacity to implement its recommendations.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".