Clashing Classes Down Under-Evaluating Australia‘s Competing Class Actions Through Empirical and Comparative Perspectives
Bibliographic record
Abstract
Australia’s three class action regimes do not provide any guidance with respect to the appropriate approach to adopt when different law firms file separate class actions with respect to the same legal disputes. The potentially adverse effects of competing class actions and the most appropriate strategies for dealing with them have been among the most important and controversial issues in Australia’s civil justice landscape over the last few years. The aim of this article is to employ the findings of the first empirical study of the class action regimes that operate in the Federal Court of Australia and the Supreme Court of Victoria to explore Australia’s experience with competing class actions, including the legal disputes and circumstances that have resulted in competing class actions and the steps (if any) that were taken – by the court and/or the lawyers in question – to deal with the problems that may be caused by competing legal representation. These findings will be compared with the Canadian experience with competing class actions. Numerous references to the American competing class actions landscape will also be made.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".