Regulating food-based agrofuels: The prospects and challenges of international trade rules
Bibliographic record
Abstract
This article considers the potential for strategic and selective use of World Trade Organization (WTO) rules to regulate, and potentially curb, the expansion of food-based agrofuels. Since 2008, a global agrofuel complex has emerged that is characterized by government-led mandates and investment for food-based agrofuel production and trade. The majority of world agrofuel production utilizes basic foodstuffs–sugar, corn/maize, soy and palm oil–thus generating competition between food/feed and fuel end-uses. This competition is strongly linked to food price volatility, food insecurity and land grabbing on a global-scale. Food-based agrofuel production is projected to increase significantly over the next decade, with international trade of agrofuels growing in tandem due to rising global demand. Despite well-documented social and ecological consequences associated with food-based agrofuels, producing and consuming states demonstrate a lack of political will to curb future agrofuel expansion and, in particular, continue to resist demands by global civil society and other social groups for global agrofuels regulation. In a global political economic context best characterized by a global governance gap for agrofuels, I consider the prospects and challenges of strategic and selective application of WTO rules to regulate food-based agrofuels.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".