Disentangling Conflicts Of Laws In EU And Member States’ Investment Agreements
Bibliographic record
Abstract
The European Union ("EU") is integrated into global markets via an open investment regime, which has fostered the development of wide economic relations. In 2019, the net investment outflow from EU Member States toward third countries totaled $42,6761 million, while inflow totaled $47,3196 million. To regulate investment disparities since the establishment of the common market in the 1950s, EU Member States have concluded about 1400 multilateral investment treaties ("MITs") and bilateral investment treaties ("BITs") with third countries. EU Member States have also negotiated around 190 MITs and BITs inter se, or intra-EU investment agreements. Since the adoption of the Lisbon Treaty in 2009, the EU has negotiated international investment agreements with economies such as Australia, Canada, China, Vietnam, Singapore, and the United States. Among these agreements, the Energy Charter Treaty ("ECT") is both an intra-EU and extra-EU investment agreement, to which both the EU and Member States are parties. It is therefore of critical importance to establish a predictable legal framework governing investments within and outside of the EU.
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 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 | 0.024 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".