Contributions and Limitations of Empirical Research on Independence and Impartiality in International Investment Arbitration
Bibliographic record
Abstract
The use of investment treaty arbitration to decide public law raises concerns about judicial independence and impartiality. These concerns arise from the absence of institutional safeguards of independence that are otherwise present in public law adjudication at the domestic or international level. In this article, opportunities to use empirical methods to study possible bias in investment arbitration are surveyed. The discussion includes a brief consideration of qualitative methods and a critique of two quantitative studies on outcomes in investment arbitration. The discussion then turns to the methodology of an ongoing project involving legal content analysis of decisions by investment treaty tribunals. The main conclusion reached in the paper is that empirical research can make important contributions to scholarly understanding of investment arbitration. On the other hand, empirical research has important limitations in its ability to demonstrate the presence or absence of actual bias, even at a systemic level, thus reinforcing the need for institutional safeguards.
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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 | 0.148 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".