Arbitrator Behaviour in Asymmetrical Adjudication (Part Two): An Examination of Hypotheses of Bias in Investment Treaty Arbitration
Bibliographic record
Abstract
This article reports on a study of potential systemic bias in the resolution of ambiguous legal issues by investment treaty arbitrators. It outlines tentative but significant findings that the arbitrators in general tended to favour (1) foreign investors over states overall, (2) foreign investors from major Western capital-exporting states over other foreign investors, and, albeit based on more limited data, (3) the United States as a respondent state over other respondent states. The evidence is derived from an extensive content analysis of the arbitrators’ resolution of fourteen legal issues that are contested among arbitrators or in secondary literature. The findings clearly support initial expectations of systemic bias arising from unique incentives of the arbitrators. Yet the study also has important limitations and there is a range of possible explanations for the findings, some not raising concerns of inappropriate bias. Broadly, the findings lend support to perceptions that the design of investment treaty arbitration does not support fair and independent adjudication of the boundaries of sovereign authority and of disputes involving public funds.
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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 | 0.105 | 0.354 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".