The Systemic Challenge of Corporate Investor Nationality in an Era of Multinational Business
Bibliographic record
Abstract
This Article examines the attribution of corporate nationality in investment treaties, particularly in the context of multinational enterprises. It traces the treatment of corporate investor nationality seen in various arbitral awards issued pursuant to international investment agreements as well as in broader public international law. The author argues that use of place of incorporation alone to determine nationality is an outdated approach given the current role of multinational enterprises in world economy. Exclusive use of this test, which disregards factors such as foreign corporate control, or lack of economic presence, allows investment treaties to serve as portals for corporate investors with nationalities of convenience. This result should either be acknowledged by states as a deliberate policy initiative, or nationality definitions should be altered and made more comprehensive in order to address this phenomenon. A downside of the use of place of incorporation alone to determine nationality in investment treaties is the risk of parallel proceedings and double recovery as well as the risk of claims by de facto local investors. Use of place of incorporation alone to determine nationality renders expansive states’ prior consent to arbitrate, and investors’ standing to launch claims. Neither denial of benefits clauses, nor arbitrators’ application of the doctrine of abuse of right, are sufficient in themselves to narrow the wide scope of states’ prior consent or investors’ standing to arbitrate which is established by use of place of incorporation alone as nationality test.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".