Fairness in class action settlements
Bibliographic record
Abstract
To be made effective, class action settlements must be negotiated fairly, be perceived as fair and reasonable by the settlement parties such that they agree to their terms and substance, and be characterized as fair, reasonable and adequate by a court at the occasion of a settlement approval hearing. But how is settlement fairness defined, in a collective litigation context? By which process is the evaluation of fairness made and the approval given by the court? What role does the court correspondingly have, in that context? This thesis explores the legal policy and reasoning behind the mandatory judicial approval of class settlements, the process by which it is sought and obtained, the currently relevant factors and indicia of settlement fairness which support all decisions to approve, and the roles of the principal settlement actors, particularly the settlement judge. It suggests hypotheses for reform applicable to these approval processes, roles of the actors and standard of settlement fairness. These hypotheses are tested, for their plausibility, against empirical data obtained from the qualitative interviews of seventeen judges conducted by the author in four target jurisdictions that have similar approaches to class action settlement approvals, and where class action litigation activity is heavy: Quebec, Ontario, British Columbia, and the United States federal courts. Ultimately, the thesis proposes final recommendations for reform of the class action settlement approval procedure.
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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 | 0.066 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.038 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| 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".