Bibliographic record
Abstract
The purpose of this article is to discuss how class action members experience access to justice in class actions, and how one may innovate in order to obtain a more complete and holistic access to justice in the context of class actions. For this purpose, six individuals were interviewed by the Class Actions Lab, at Universite de MontreaI; two of these individuals were class action members and four were class action representatives. They were asked generally about their level of involvement in the proceedings' decision-making processes and their perception of justice and satisfaction with the overall outcome of the proceedings. The data collected illustrates the correlation between adequate representation and enhanced access to justice for class members. The article concludes by presenting ideas gathered from the interviewed class representatives and members on how to improve access to justice in class actions.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.035 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".