Damage Averaging and the Formation of Class Action Suits
Bibliographic record
Abstract
Within a class action suit, similarly injured individuals can collectively obtain compensation through the justice system. Damage averaging occurs when the compensation awarded by the court to individual members is partly or completely determined by the average damage of the class. The key role of damage averaging in influencing the identity of the individual that will initiate the class action suit is illustrated in a waiting game. If there is complete averaging, the individual with the lowest damage will initiate the class action suit, while if there is less damage averaging, other individuals may do so. Grâce au recours collectif, des individus ayant subi des dommages d'ampleur différente mais de même nature peuvent obtenir compensation en cour. Il est possible que le montant accordé à un individu par la cour ne soit pas strictement une compensation pour les dommages qu'il a subis, mais qu'il réflète aussi, en partie, la moyenne des dommages subis par tous les participants au recours collectif. Envisageant la formation d'un recours collectif comme un jeu d'attente, nous montrons que l'usage de la moyenne des dommages par la cour est un déterminant important de l'identité de celui qui initiera le recours collectif. Si seule la moyenne des dommages est utilisée par la cour dans l'établissement des compensations, alors l'individu ayant subi les plus petits dommages initiera le recours collectif. Si la cour utilise également les dommages individuels dans l'établissement des compensations, alors d'autres individus pourraient vouloir l'initier.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".