Evaluating an International Investment Court for International Investment Disputes Under European Union’s Proposal
Bibliographic record
Abstract
Berserk resentment of the existing framework regulating the international investment protection system and the operating of investment tribunals have direct to a prevalent perception that there is an immediate need for reform. This is especially pronounced having to do with Investor-State dispute settlement (ISDS), where there is an overall perception that it is not anything but an unfair and unbiased arbitration system available to decide disputes between states and foreign investors. Therefore, ISDS has been obtained a reputation for being non-transparent, one-sided, and contradictory in all decisions made by ISDS tribunals. The European Union (EU) has responded to this need, by proposing an international investment court; in this research, an attempt is making to look at this court, according to the European Union’s proposal. Moreover, the research explores the potential in creating this international investment court since a system can be drastically altered. However, some criticism can be addressed by international investment courts. However, specific steps can be taken to improve the international community’s investor-state dispute settlement system by re-valuating all the objectives and goals to solve international investment disputes.
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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 | 0.069 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.034 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.025 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".