After the Windfall: Representation of Class Members in Individual Issues Adjudication
Bibliographic record
Abstract
Class members must receive adequate information and representation for the adjudication of individual issues. This paper examines the circumstances that give rise to a need for information and representation, and suggests some possible approaches for providing it. Settlement administrators have developed strategies to effectively prepare class members to prove their cases, but this does not completely obviate the need for representation. Courts are beginning to recognize these issues at the certification or settlement approval stage by insisting that class counsel continue to represent individual class members. Pursuant to some recent decisions, counsel must build the representation of individual class members into their retainer, or risk the settlement failing to win approval. Two other methods of ensuring that class members have access to representation and support are considered. The first method involves deferring a portion of class counsel’s fee until the take-up rates are known. This procedure provides an incentive for counsel to remain involved in individual files, and has been applied by some Canadian courts. The second method is not in the hands of the courts. Rather, it envisions class members helping themselves by organizing support networks.
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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 | 0.081 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".