The Downside of Preclusion: Some Behavioural and Economic Effects of Cause of Action Estoppel in Civil Actions
Bibliographic record
Abstract
The primary objective of the present article is to draw attention to the drawbacks of preclusion, especially of the rules of cause of action estoppel. The article challenges the traditional assumption that the rule of cause of action estoppel increases efficiency by introducing some economic and behavioural effects of the rule, especially the effects of the rule against splitting a single claim or cause of action. Analysis of the effects of cause of action estoppel has three major methodological goals: (a) to re-examine the rule in light of the behaviour modification model, (b) to evaluate the economic efficiency of the rule and its effect on the cost of litigation, and (c) to consider the influence of the rule on the chances of reaching a settlement. The article discusses the problematic incentives of litigating parties under the current Anglo-American rule of cause of action estoppel, and some of its harmful effects on the conduct and cost of litigation as well as on the chances of reaching a settlement. The article shows that, in many cases, the cause of action estoppel rules have undesirable effects on the conduct of litigation, including stimulating overlitigation in the initial action. Furthermore, the rule against splitting a single cause of action does not always contribute to an economically efficient legal system, and reduces the chances of reaching a settlement, which has a harmful effect on both the economic and behavioural aspects of litigation. By contrast, allowing the splitting of a single cause of action can significantly increase the litigants’ incentives to settle, providing the parties with opportunities for employing useful settlement strategies.
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".