Discourses of ISDS reform: a comparison of UNCITRAL Working Group III and ICSID processes
Bibliographic record
Abstract
Abstract The reform of investor-state dispute settlement (ISDS) has been tackled by the International Centre for Settlement of Investment Disputes (ICSID) and United Nations Commission on International Trade Law (UNCITRAL) Working Group (WG) III. Despite different objectives, both processes have relied on written submissions from various stakeholders. What are the structures and the narratives underlying the discourses of ISDS reform in these organizations? This article explores the content of 172 submissions by using mixed methods. It demonstrates that UNCITRAL WG III has involved less structured submissions whose content has expanded the initial mandate, with narratives encapsulating deeper disagreement among participants. By contrast, ICSID operated through a common pattern across submissions and a stronger focus on procedural issues, with less disagreement revealed in its narratives. The article proceeds in three steps. First, it compares the structure of discourses for each reform process by aggregating the content of submissions through computational analysis. Second, it relies on critical discourse analysis to reveal narratives that have emerged in each process. Lastly, the article explores submissions from actors who have participated in both processes to illustrate how they have navigated the tension between structures and narratives when reforming international investment arbitration.
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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 | 0.070 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".