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Record W1778703268 · doi:10.15353/cfs-rcea.v2i2.137

GFT - SYNTHESIS - The uneasy relationship between international trade and agriculture

2015· article· en· W1778703268 on OpenAlexaffvenue
Kim Burnett

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 institutionsBalsillie School of International AffairsUniversity of Waterloo
Fundersnot available
KeywordsAgricultureIntellectual propertyInternational tradeAgreement on AgricultureWorld tradeTRIPS architectureSovereigntyInstitutionPolitical scienceBusinessEconomicsLawGeographyEngineering

Abstract

fetched live from OpenAlex

In his 2006 book, Food is Different, Peter Rosset posited we “get agriculture out of the [World Trade Organization] WTO”. This contention, which is the rallying cry for the Food Sovereignty movement, is that the WTO should not have any purview over agriculture and by extension food systems. Getting the WTO out of agriculture encompasses not only dismantling the 1994 Agreement on Agriculture, which governs both global food trade and extends to national food policies, but also nullifying the entire suite of WTO agreements that apply to various aspects of agriculture, including the Agreement on Trade-Related Intellectual Property Rights (TRIPS), and the Agreement on Trade-Related Investment Measures (TRIMS) (see Burnett & Murphy, 2014). For activists, policymakers, and scholars who take a firm stance of resistance to the WTO, there is no room for compromise with the institution. From this standpoint, the WTO cannot be transformed into a legitimate space to govern international food trade. The underlying concerns motivating much global civil society resistance to agriculture being governed under the WTO are well documented in the papers in this collection and were discussed at length by participants at the workshop in Waterloo.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.974

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 studies0.0010.000
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 head0.086
GPT teacher head0.237
Teacher spread0.151 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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