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Record W2022829871 · doi:10.1108/17542431111147783

Lessons learned from a comparative examination of global civil justice reforms

2011· article· en· W2022829871 on OpenAlexaboutno aff
Shahla F. Ali, Felicia Lee

Bibliographic record

VenueInternational Journal of Law and Management · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicConflict of Laws and Jurisdiction
Canadian institutionsnot available
Fundersnot available
KeywordsOriginalityScope (computer science)Economic JusticeMediationCivil procedureAlternative dispute resolutionValue (mathematics)Civil societyDispute resolutionPublic administrationPolitical scienceJudicial reformSociologyLawLaw and economicsPublic relationsComputer science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine the impact of recent civil justice reforms in five jurisdictions including Singapore, Malaysia, Hong Kong, the UK and Canada on the resolution of civil and commercial disputes. Design/methodology/approach The study, drawing on a comparative cross‐jurisdictional methodology, reviews the scope and nature of such reforms and examines lessons learned regarding implementation. Findings The findings of the research indicate that such reforms are most effective where regular evaluation to fine‐tune mediation rules occurs concurrently and in conjunction with the implementation of such reforms. Research limitations/implications The limitation of this research is that it is confined to already existing court case statistics, judicial commentaries and reviews of the five selected jurisdictions. Practical implications The practical implications of the study find that in general, civil justice reforms have made some progress in achieving the aims of encouraging cost‐effective, expeditious and amicable case handling within the civil justice system. Originality/value The paper contributes to a global analysis of effective approaches to civil justice reform and in particular reforms in mediated case handling.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.133
GPT teacher head0.383
Teacher spread0.251 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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