Bibliographic record
Abstract
Third-party funding is a controversial business arrangement whereby an outside entity—called a third-party funder—finances the legal representation of a party involved in litigation or arbitration or finances a law firm’s portfolio of cases in return for a profit. Attorney ethics regulations and other laws permit nonlawyers to become partial owners of law firms in the District of Columbia, England and Wales, Scotland, Australia, two provinces in Canada, Germany, the Netherlands, New Zealand, and other jurisdictions around the world. Recently, a U.S.-based third-party funder that is publicly traded in England started its own law firm in England. In addition, some U.S. law firms are actively seeking advice (including from this Author) regarding partnering with third-party funders or starting their own internal thirdparty funders to fund their own cases, both of which are controversial practices. This Article analyzes the benefits and drawbacks of third-party funders becoming internal partners of U.S. law firms, rather than remaining as external investors. To that end, this Article diagrams the existing structure of the third-party funding transaction and suggests new possible structures. This Article then explores how those new structures may affect procedure, evidentiary, and ethics rules and reshape both the third-party funding industry and the legal services industry. This Article concludes that careful, limited experimentation would reveal whether such a practice is a viable, desirable addition to the menu of third-party funding transactions or whether the existing third-party funding transaction paradigm remains the best option. Ultimately, this Article aims to start a conversation about rethinking the structure of third-party funding transactions.
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 | 0.031 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".