The Effect of Opinion 1/17 on the EU-ECHR Draft Accession Agreement: Lessons Learned?
Bibliographic record
Abstract
Opinion 2/13, in which the Court infamously declared the first draft agreement on the accession of the EU to the ECHR not to be compatible with the Treaties, is one of the clearest examples of the CJEU’s protectiveness in its external relations case law. Now, ten years later, this very draft agreement has been revised and might again be subjected to the scrutiny of the Court. However, what has changed in the meantime is what scholars consider a softening in the Court’s otherwise rather rigid approach to autonomy. Opinion 1/17 on the Comprehensive Economic and Trade Agreement (CETA) with Canada did indeed come as a surprise to some, as the Investor state dispute settlement (ISDS) system that it establishes was considered to be in line with the notion of autonomy. Naturally, the question arises of whether the CJEU, if asked to rule again on the legality of the DAA concerning the EU’s accession to the ECHR, would follow its conciliatory approach vis-à-vis the international legal order, which it embarked on in Opinion 1/17. Therefore, the present contribution will seek to answer two related questions: Which lessons can be derived from Opinion 1/17, and did the negotiators take these lessons into account in the revised DAA?
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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 | 0.019 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".