Bibliographic record
Abstract
This Article explores the tensions between the perceived public and private aspects of the litigation system by using the debate surrounding whether or not the public civil justice system can and should tolerate secret settlements in standard, nonaggregate private law disputes.By evaluating the arguments against secret settlements in this context, the Article argues that the public and private aspects are not exclusory, oppositional perspectives but instead lie on a nonlinear continuum in which both may inform the other.The proper place on the continuum on which a certain procedural solution may lie often depends upon what type of dispute is at the center of the debate.When reforming and designing the civil justice system, either in whole or in part-like whether or not to allow secret settlements-one can expect a fuller, more balanced normative dialogue by thinking of the private and public views of civil litigation as operating on a nonexclusive and nonlinear continuum.This Article delves behind the seemingly oppositional perspectives of the public and private conceptions of civil litigation to reveal the utility of thinking about the two not as exclusive but cohesive.A decidedly private view of secret settlements does not, in all instances, negate the concerns of a view closer toward the public side of the continuum.In the end, the private view of litigation often has far more "public" to the "private" than one might expect.I. WHAT ARE SECRET SETTLEMENTS?....
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.020 |
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".