Financing Disputes: Third-Party Funding in Litigation and Arbitration
Bibliographic record
Abstract
Third-party funding is an arrangement where an entity with no prior interest in the merits of a dispute provides funding to a party involved in the dispute. Traditionally, this funding was specifically to assist the party to the dispute by financing its legal fees and costs and could be obtained in a number of ways, such as through insurance or loans from financial institutions. Third-party funding has seen significant growth and an increase in sophistication in recent years, resulting in a departure from this traditional model concurrent with the rise of commercial litigation funders whose entire business is providing non-recourse investment in disputes. This article explores both the changes in models of third-party funding — which can include some or all of: (1) paying for legal fees and disbursements, (2) indemnifying against the risk of an adverse costs order, (3) stepping in to provide security for costs, (4) providing working capital or portfolio funding for bundles of claims, and (5) the rise of institutional third-party financing in Canada. In particular, this article will explore some of the specific applications of third-party funding to the energy industry, including “David and Goliath” claims, claims involving state asset expropriation, and the use of funding as a tool for risk allocation in asset sales. This article will also discuss the development and current state of the legal framework and case law in Canada with respect to third-party funding, along with third-party funding across different contexts and types of disputes. This includes the evolution of the law of maintenance and champerty and a discussion of key legal and ethical issues engaged by third-party funding arrangements including confidentiality, privilege, disclosure, conflicts of interest, and control of the dispute.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".