Contingency Fee Agreements with Represented Persons in Class Actions—An Undesirable Australian Phenomenon
Bibliographic record
Abstract
Part IVA of the Federal Court of Australia Act 1976 (Cth), which has been regulating class actions in the Federal Court of Australia since 1992, is silent with respect to the crucial issue of whether the lawyers hired by the class representatives may enter into contingency fee agreements with such representatives and/or the persons on whose behalf the class proceedings are instituted, the class members. This silence was attributable to the Australian Government's rejection of the Australian Law Reform Commission's recommendation that the legislative regime governing class actions should expressly authorise and regulate the execution of contingency fee agreements by the class lawyers with the class representatives. As a result of several post–1992 statutes enacted by State Parliaments, lawyers hired by Part IVA plaintiffs have been able to follow the practice of executing contingency fee agreements with, not only the representative plaintiffs, but also the class members. The aim of this paper is to provide a critical analysis of this practice. It undertakes a review of the American and Canadian regimes governing the employment of contingency fee agreements in class proceedings as a part of this analysis.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".