Disclosure of Third-Party Funding in International Arbitration
Bibliographic record
Abstract
Third-party funding has evolved into a ubiquitous “feature of modern litigation” that in some jurisdictions is “an accepted and judicially sanctioned activity perceived to be in the public interest.”529 Similarly, third-party funding has become even more prevalent in international arbitration, particularly considering the high dollar amount of most arbitral awards. In addition, several major arbitration seats have officially embraced third-party funding in international arbitration through legislation or court opinions, including Australia, England, and Wales, most of the states in the United States, Germany, the Netherlands, several provinces in Canada, Singapore, Hong Kong, South Africa, and Nigeria (indirectly).530 Furthermore, there are many other jurisdictions where third-party funding may be happening, but no official governmental response has yet ensued. This article proceeds as follows. The remainder of this introduction defines third-party funding, describes basic third-party funding transaction structures, and outlines the major debates surrounding the existence of third-party funding in international arbitration. Next, this article outlines the reasons and scope for disclosure and describes rules and guidelines for third-party funding as articulated by institutions, arbitral tribunals, domestic courts, treaties, and domestic legislation. This article then addresses third-party funders as custodians of confidential information and charges them with ensuring the legitimacy of the arbitration process and preventing arbitrator conflicts of interest. Finally, this article addresses the rising influence of “outcome-motivated” (or not-for-profit) funders, whose primary focus is something other than making a financial profit from the case.
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 | 0.033 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".