Lessons learned from a comparative examination of global civil justice reforms
Bibliographic record
Abstract
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 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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".