Bibliographic record
Abstract
One of the foundational principles of legal ethics is that the lawyer owes an obligation of undivided loyalty to the client, and no other interests or relationships can be permitted to interfere with the lawyer’s exercise of independent professional judgment on behalf of the client. The strongest non-consequentialist doctrinal objection to third-party litigation funding is that it may compromise the lawyer’s independence. Yet this argument cannot be made in too strong a form, because lawyers are already permitted to enter into relationships or have interests that present a prima facie risk to the lawyer’s independence. In the United States, two such situations are the representation of plaintiffs in contingent-fee financed litigation and the representation of insured defendants by lawyers compensated, and substantially controlled, by liability insurers. Both of these situations present conflicts of interest that are mitigated for the most part not by formal rules of professional conduct but by other legal and non-legal sources of constraint. In the insurance defense context, many apparent conflicts are mitigated by doctrines within insurance law that limit the extent to which insurers can act self-interestedly at the expense of the insured. Regarding contingent-fee representation, market mechanisms are entrusted with the role of regulating the size of fees, while agency and tort principles regulate the conduct of lawyers representing plaintiffs in contingent-fee matters. These comparisons show that third-part litigation finance should not be condemned categorically as compromising the lawyer’s independent judgment. Rather, the acceptability of third-party finance should be dependent upon the extent to which the relationship between the funder and the recipient of funding is regulated to mitigate the risk of self-interested behaviour on the part of the funder. Forthcoming in a symposium in the Canadian Business Law Journal on alternative litigation financing.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.019 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".