EU External Relations Law on Trade and Sustainable Development: The Evolving Nature of EU Law and Policy Constraints
Bibliographic record
Abstract
EU External Relations Law and Policy significantly evolved since the seminal Singapore Opinion in the Court of Justice (ECJ). This paper analyses the new trade and sustainable development (TSD) policies by the European Union (EU) and highlights how the changes at the EU domestic level in the course of the EU Green Deal have fundamentally changed the outlook for EU external action and even changed EU External Relations Law in the process. The use of constitutional objectives in external relations is now firmly established but the new mix of autonomous and cooperative elements also changes the more fundamental principles on which EU foreign relations are based.While the EU is still finding the right balance between autonomous measures and international cooperation, the new flexibility, particularly to address global sustainable development challenges and in the context of the Sustainable Development Goals (SDGs) allow the EU greater foreign policy flexibility in areas covered by global targets such as the SDGs, the Paris Agreement and the new Convention on Biological Diversity (CBD) Kunming-Montreal Global Biodiversity Framework. It is argued that the existence of global goals and targets combined with EU internal action in the context of the EU Green Deal fundamentally change the availability and legality of EU External Relations Law on Trade and Sustainability.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| 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".