Defining Civil Disputes: Lessons from Two Jurisdictions
Bibliographic record
Abstract
Court systems have adopted a variety of mechanisms to narrow the issues in dispute and expedite litigation. This article analyses the largely unsuccessful attempts in two jurisdictions - the United States and Australia - to achieve early and efficient issue identification in civil disputes. Procedures that rely on pleadings to provide focus have failed for centuries, from the common (English) origins of these two systems to their divergent modern paths. Case management practices that are developing in the United States and Australia offer greater promise in the continuing quest for early, efficient dispute definition. Based on a historical and contemporary comparative analysis of the approach to pleadings in the United States and Australia, this article recommends that courts should rethink the function of pleadings, alter litigation incentives, and refine case management practices. This will lead to earlier issue identification, better framing of the discovery process, and a more efficient litigation process.
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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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.061 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.008 | 0.012 |
| 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".