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Record W1595734692

Defining Civil Disputes: Lessons from Two Jurisdictions

2011· article· en· W1595734692 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSMU Scholar (Southern Methodist University) · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDispute Resolution and Class Actions
Canadian institutionsUniversity of WindsorDalhousie University
Fundersnot available
KeywordsFraming (construction)Variety (cybernetics)Dispute resolutionCivil litigationIncentivePolitical scienceCivil procedureIdentification (biology)LawCommon lawLaw and economicsEngineeringSociologyEconomicsComputer scienceCivil engineering
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.004

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.

Opus teacher head0.041
GPT teacher head0.249
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it