Leaders in the Expansive and Restrictive Interpretation of Investment Treaties: A Descriptive Study of ISDS Awards to 2010
Bibliographic record
Abstract
This article provides an empirical analysis of interpretive discretion in investor–state dis- pute settlement (ISDS). Since the late 1990s, foreign investors have brought hundreds of investment treaty claims against states, leading to numerous awards in which arbitrators have interpreted investment treaties. Arbitrators may resolve ambiguities in the treaties in expansive or restrictive ways, thereby affecting the compensatory promise of ISDS for foreign investors and corresponding risks for states. Which arbitrators have contributed most to expansive or restrictive approaches? To examine this question, data was analysed on arbitrators’ resolutions of contested legal issues, such as the permissibility of parallel or minority shareholder claims and the scope of concepts of investment, fair and equita- ble treatment, full protection and security and indirect expropriation. The analysis allows for rankings of arbitrators and tentative descriptive findings identifying a small group of individuals as the leading contributors to expansive resolutions and one individual as the leading contributor to restrictive resolutions. The analysis reveals how interpretive discre- tion impacted relevant legal aspects of ISDS in its first two decades and supplements other research on ISDS arbitrator behaviour.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".