Sustainable Food System Chapters in Trade and Investment Agreements: Lessons on Policy Innovation
Bibliographic record
Abstract
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.
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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 | 0.050 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".