The Role of the Domestic Law of the Host State in Determining the ratione materiae Jurisdiction of Investment Treaty Tribunals: The Partial Revival of the Localisation Theory
Bibliographic record
Abstract
This Thesis begins by examining the general role that relevant domestic laws (of the capital-importing and capital-exporting countries) play in the determination of contested jurisdictional and substantive issues in investment treaty arbitrations. Subsequently, in two separate chapters, the Thesis delineates the legal bases for the application and the specific function of the municipal laws of the recipient state in settling two paramount issues of jurisdiction ratione materiae in investment treaty arbitrations, namely the matters concerning the legality of the investment as well as the existence of ‘property rights’ underlying the ‘investment’. Having pinpointed the vast relevance of the internal laws of the host state in deciding upon these jurisdictional thresholds, the Thesis goes on to submit that this extensive role of the host state law in the above-mentioned matters coupled with other circumstances and developments in the international law of investment (in particular, the ever-increasing role of the courts of the host state in the resolution of investment treaty arbitration disputes) tend to partially revive the long-dormant theory of ‘localisation’ which had once been substantially affected in a negative way by the inception and the introduction of bilateral investment treaties.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".