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Record W1787573598 · doi:10.15353/cfs-rcea.v2i2.82

Regulating food-based agrofuels: The prospects and challenges of international trade rules

2015· article· en· W1787573598 on OpenAlexvenueno aff
Matias E. Margulis

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
Fundersnot available
KeywordsFood securityInternational tradeContext (archaeology)Competition (biology)BusinessFood processingFood systemsEconomicsAgriculturePolitical scienceBiology

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.226
Teacher spread0.144 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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