A Canadian Lens on Third Party Litigation Funding in the American Bankruptcy Context
Bibliographic record
Abstract
This Article offers two major recommendations to expand the use of third party litigation funding ("TPLF") into the U.S. insolvency context. As seen in the Canadian context, courts have accepted the use of litigation funding agreements fitting within certain parameters. If U.S. courts follow suit, friction against the implementation of TPLF can be mitigated. Alternatively, regulation may occur through legislative and regulatory models to govern and set out precisely what types of arrangements are permitted. Involving entities such as the SEC may expedite the acceptance of TPLF, but special attention is necessary not to intermingle notions of fiduciaries into the discussion of TPLF, as there are contentious definitional elements present. Ultimately, a framework wherein regulation coupled with judicial oversight presents the best opportunity for the United States to adopt TPLF in the insolvency context to ensure maximum delivery of benefits to vulnerable parties.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".