Third-Party Funding in International Arbitration, Second Edition
Bibliographic record
Abstract
About this book:\nThird-Party Funding in International Arbitration expertly reveals the nuances of third-party funding in international arbitration, examines the phenomenon in key jurisdictions, and provides a reliable resource for users and potential users who may wish to tap into and make use of this distinctive funding tool. Since the first edition of this invaluable book in 2012, third-party funding has become more mainstream in international arbitration practice. The second edition includes discussion of recent institutional developments as they relate to third-party funding, including the work of the ICCA-Queen Mary Task Force on third-party funding and how third-party funding is being incorporated into arbitral rules and investment treaties.\nWhat’s in this book:\nFocusing on Australia, the United Kingdom, the United States, Germany, the Netherlands, Canada, and South Africa, the authors analyze and assess the legal regime based upon legislation, judicial opinions, ethics opinions, and practitioner anecdotes describing the state of third-party funding in each jurisdiction. In addition to updating summaries of the law of the various jurisdictions, the second edition includes a new chapter addressing third-party funding in investor-state arbitration. Among the issues raised and examined are the following: payment of adverse costs; “Before-the-Event” and “After-the-Event” insurance; attorney financing: pro bono representation, contingency representation, conditional fee arrangements; loans; ethical doctrines affecting the third-party funding industry; possible future bundling, securitization, and trading of legal claims; the risk that the funder may put its own interests ahead of the client’s interests; and whether the existence of a funding agreement must or should be disclosed to the decision maker. \nThis updated book is well written and covers an interesting, topical, and previously unexplored aspect of international arbitration, and it concludes with observations regarding third-party funding in international investment arbitration and predictions regarding the future of the third-party funding industry worldwide.\nHow this will help you:\nThis book ensures appropriate legal knowledge of worldwide jurisdictions’ treatment of third-party funders and proceeds confidently through the maze of unresolved ethical considerations that arise when third-party funding is involved. Ably providing a thorough understanding of what third-party funding entails and what legal parameters exist, this book will be of compelling interest to parties aiming to take advantage of the high values, speed, reduced evidentiary costs, industry expertise, and high award enforceability characteristic of the third-party funding arrangements available in international 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.018 |
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".