Defending the State in ISDS and Preventing Disputes
Bibliographic record
Abstract
This chapter analyses the policies and practices related to resolving investor–state disputes through ISDS. In this area, three broad themes emerged from our data. First, there are practices of forming the defence strategy for specific investment arbitrations and handling ISDS proceedings. The main issue is whether to engage lawyers from private practice and, if so, to what extent. Second, we discuss the matter of coordination and communication between various governance actors during ISDS proceedings. The third issue is that of dispute prevention. Given the stakes, risks, and challenges resulting from ISDS disputes, many governance actors dealing with IIAs realise that dispute prevention is crucial in internalising the IIA disciplines. This section focuses on various training and educative programmes for bureaucrats that were designed, proposed, or implemented to increase the knowledge about and awareness of IIAs within the broad sphere of national governance. We end with a discussion on the blurring of the public–private divide through the engagement of private expertise in the service of the public when defending ISDS cases.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".