The European Parliament as a Defender of EU Values in EU-Japan Agreements: What Role for Soft Law and Hard Law Powers?
Bibliographic record
Abstract
This article investigates to what extent the European Parliament (hereafter the Parliament) acted as an advocate for EU values in the development of EU-Japan relations, through which legal tools, if hard law or soft law powers, and with what legal outcomes. Japan is an interesting, yet underexplored, case study for assessing the external reliance of EU values. It is a key transversal partner for the EU with 3, potentially 4, agreements concluded across trade, security and political cooperation sectors (the 2009 MLA agreement, the 2018 Strategic Partnership Agreement and Economic Partnership agreement and a PNR exchange agreement currently being negotiated), but which presents important values differences with the EU, e.g., on death penalty and data protection. Our findings show that, with Japan, the Parliament has stepped away from its traditional role as a human rights defender, by sticking to soft law powers as its privileged tool, limiting its interventions, and ultimately refraining from insisting trade be linked to human rights commitments. Such a cautious approach, we argue, is the result of a deliberate choice to keep negotiations with Japan in an economic prism, and to invest negotiation energy in more salient negotiations occurring at the time in which the EU-Japan agreement was negotiated, e.g SWIFT and Brexit TCA. The overall judgement on the Parliament's approach is, however, a nuanced one. Its reliance on soft law powers might be a wise one for the time being and might encourage other actors to litigate on points which were compromised on, given that its previous use of hard law powers with the US and Canada PNR agreements somehow backfired. Moreover, the sole presence of hard law powers can, and has influenced other actors to adjust their positions during the negotiations to pre-empt the use of the Parliament’s veto powers.
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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 | 0.028 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".