Justice for Profit: A Comparative Analysis of Australian, Canadian and U.S. Third Party Litigation Funding
Bibliographic record
Abstract
Third party litigation funding (TPLF) has emerged as one of the most important developments in civil litigation. Courts and policymakers in several countries are looking to each other as they debate the costs and benefits of this growing industry and the need for regulatory oversight. Such cross-pollination in the public and jurisprudential debates on TPLF can be enormously helpful, but must be approached with caution. The TPLF industry operates in very different procedural environments, and any comparative analysis must take into account the various jurisdictions' unique litigation culture and architecture. In this paper, the authors explore TPLF in the United States, Australia and Canada, with a focus on class action litigation in the latter two jurisdictions. They examine the historical development of TPLF, current practices, the legal and procedural context within which such funding takes place, and how each jurisdiction is addressing regulation of this form of finance. In the final part of the paper, they engage in a comparative analysis of TPLF in the three countries, and highlight important differences that may ultimately result in unique approaches to regulatory oversight of the industry. “For us to have access to fairly priced funding would enormously improve access to justice.” “Third-party funding undermines the civil justice system. […] Do we really want [funders] in our civil justice systems to commercialise the practice of law? We are moving from being a profession into being an investment entity.”
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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".