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Record W4410827398 · doi:10.1111/1758-5899.70028

Sustainable Food System Chapters in Trade and Investment Agreements: Lessons on Policy Innovation

2025· article· en· W4410827398 on OpenAlexaff
Dori Patay, Holly Rippin, Kelly Garton, Ashley Schram, Paz Belen Cavada‐Robert, Wolfgang Alschner, Camila Corvalán, Anne Marie Thow

Bibliographic record

VenueGlobal Policy · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Ottawa
FundersNational Health and Medical Research CouncilUniversity of SydneyMedical Research CouncilAustralian Government
KeywordsInvestment (military)BusinessInternational tradeEconomicsPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT The need to adapt existing global policy instruments to achieve sustainable development objectives is increasingly recognised worldwide. Responding to the global need to help countries progress towards transforming food systems, the EU proposed, negotiated and agreed to adopt a chapter dedicated to Sustainable Food Systems in its free trade agreement with New Zealand and its advanced framework agreement with Chile. This study aimed to identify the origins, rationale and enablers of this policy innovation. A theory‐informed qualitative study methodology was applied based on interviewee data. We found that the idea of the Sustainable Food System Chapters originated from the need to respond to domestic and global pressures to maintain public and political support for pursuing trade and investment agreements. The adoption of the Sustainable Food System Chapters was enabled by political pressure; pre‐existing global, foreign and domestic policies; and shared thinking that was previously translated into joint action. Policy‐makers may use this evidence to support efforts to achieve greater policy coherence in trade agreement negotiations across economic, social and environmental domains.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.059
GPT teacher head0.291
Teacher spread0.233 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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