Bibliographic record
Abstract
Abstract Correctness of arbitral awards is a central concern in current multilateral efforts to reform investor-state dispute settlement (ISDS). Aside from protecting the disputing parties from mistakes by tribunals (retrospective correctness), corrective review also guides future interpreters not to repeat past mistakes (prospective correctness). This article assesses how effective the three existing ISDS correction mechanisms – (1) review by annulment committees or domestic courts, (2) review by the contracting parties, and (3) review by subsequent tribunals – are in promoting such prospective correctness. After assessing existing practice, the article finds that wrong decisions “don’t die”. Annulled or set-aside awards continue to be cited, contracting states’ authoritative interpretations are disregarded, and subsequent tribunals do not converge around a jurisprudence constante. This failure of corrective mechanisms to achieve prospective correctness is due to lacking legal constraints, incentives to use favorable awards even if they have been invalidated, and the simple difficulty in telling whether an award still represents “correct” law in ISDS. The article concludes by proposing possible reforms to improve prospective correctness from the shepardization of awards, to rules on precedent, and broader institutional reform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.107 | 0.326 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| 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".