Bibliographic record
Abstract
Imperfect information problems afflict all litigation, but they have some particularly important effects in class action litigation that seeks redress for conspiracies that are unlawful under antitrust law.In this comrrrent, I will outline briefly some of the ways in which the presence of imperfect information can affect the analyses in the excellent articles by Margaret Sanderson and Michael Trebilcock 1 and David Rosenberg and James Sullivan in this collection.2 Both ankles offer valuable insights into the strengths and drawbacks of reliance on privately initiated class actions for conspiracy.The benefits of such private litigation efforts are that they supplement and serve as a check on sluggish public enforcement, and the costs are the risk of strike suits and over-enforcement.Imperfect information contributes importantly to the benefits and costs of class action litigation about conspiracy, as the articles suggest, but also can affect the proposed solutions to the problem.In this brief comment I discuss the two articles using imperfect information as my frame of analysis.First, I discuss problems of imperfect information in detecting conspiracies; second, I discuss problems of imperfect information in calculating damages resulting from conspiracies .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".