The international investment regime is stronger than you think: Understanding the interplay of diplomatic, insurance and legal approaches for protecting FDI
Bibliographic record
Abstract
Today the assets of foreign investors are protected by three distinct yet overlapping pillars: diplomatic pressure applied by the home state; political risk insurance purchased by the investor; and investor-host state legal arbitration. These pillars represent mutually reinforcing approaches to compensating foreign investors for the adverse effects of host state policy. This paper places these three pillars within a unified conceptual framework, and argues that their overlaps and interactions lead to stronger protections for foreign investors than much of the literature commonly assumes. The creation and institutionalization of new forms of protection for foreign investments over the last half-century have not necessarily replaced or substituted old forms of protection, but rather have complemented and added to them. Two brief case studies - of the Cora de Comstar dispute in Cote d'Ivoire and the Dabhol dispute in India - illustrate how the three pillars operate simultaneously and collectively in contemporary investment dispute settlement. The paper's findings hold important implications for both investors and states.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".