Adjudicator Compensation Systems and Investor-State Dispute Settlement
Bibliographic record
Abstract
Compensation for adjudicators is generally considered as a core issue for judicial independence and for attracting good judges in the institutional design for courts. This paper examines compensation systems for adjudicators and dispute settlement administrators in investor-state dispute settlement (ISDS). The paper uses in part a comparative perspective based on approaches in domestic courts in advanced economies, an approach rarely taken in analysing investor-state arbitration. The first section of the paper provides historical context and examines the reform of remuneration of judges to replace private litigant fees with salaries in colonial America and the United States, France and England in the 18th and early 19th centuries. Subsequent sections address debates over the impact of compensation systems on adjudicators; contemporary approaches to the compensation of judges in advanced economies; the co-existence in advanced economies of national courts with salaried judges since the early 19th century with generally strong support for commercial arbitration based on ad hoc fee-based remuneration; and similarities and differences between commercial arbitration and investment arbitration, focusing on how the largely similar compensation systems may have different effects and be differently perceived by the public. Annexes to the paper report on discussions about adjudicator compensation at the 2016 OECD Investment Treaty Conference and gather some preliminary facts about adjudicator and dispute administrator compensation in investor-state arbitration as well as in the investment court system included in the recent EU-Canada CETA trade and investment agreement.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".