Commercial Litigation Funding: Ethical, Regulatory and Comparative Perspectives
Bibliographic record
Abstract
There has been a proliferation of writing about commercial litigation funding (“CLF”) over the past few years, in both the academic and popular press. Too often, the literature presents a narrative of extremes. Commentators are either wholly against CLF on the basis that it gives rise to unethical behaviour and the commodification of our civil justice system, or wholly in support of it on the basis that it promotes access to justice and levels the playing field. A conference held at the University of Windsor Law School in July, 2013 brought together leading scholars, judges and lawyers from the United States, Australia and Canada to engage in a nuanced discussion about CLF that mediated between these extreme polarities. The first conference of its kind in Canada, discussion was focused on four themes: regulation, access to justice, ethics and impacts on class actions. In this article, the conference organizers, Dean Camille Cameron and Professor Jasminka Kalajdzic, survey the principal issues in the debates around CLF, summarize the key points in the conference papers, and identify the basic principles that might inform the regulation of litigation funding in Canada and elsewhere.
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 | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.019 | 0.073 |
| Scholarly communication | 0.032 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".