Preventing the Regulatory Chill of International Investment Law and Arbitration
Bibliographic record
Abstract
International investment law has increasingly come under attack because it does not put host states on par with foreign investors. Foreign investors can evoke broad investment rights and pursue investment arbitration. The threat of substantial arbitral awards can result in host states not enacting policies, regulations, laws or reaching decisions, despite them being needed in order to protect a variety of important public interests. The concern is, therefore, that international investment law, including the investor-state dispute resolution system, causes a regulatory chill. The paper examines how the asymmetric relationship between foreign investors and host states can be remedied, so that trust in international investment law is strengthened and its legitimacy crisis is overcome. One core issue with international investment law is that the customary international minimum standard and its therein subsumed full protection and security, and fair and equitable treatment and compensation principles are inherently vague, thereby contributing to the overprotection of foreign investors. Arbitral cases further highlight how regulatory changes can result in host states incurring liability and thus enable foreign corporations to shift potential costs and risks. International, and national solutions to prevent the regulatory chill of international investment agreements are spelled out.
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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 | 0.029 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".