Integrating Early Neutral Evaluation into Mediation of Complex Civil Cases in Malaysia
Bibliographic record
Abstract
Early Neutral Evaluation and mediation are claimed to be suitable alternative dispute resolution mechanisms for resolving a myriad of civil cases. Regrettably, very little information is known as to how viable is the combining of these two mechanisms in resolving complex civil cases in Malaysia. The purpose of this study was to explore the viability of Early Neutral Evaluation and mediation in resolving complex civil cases. This study investigated the distinctive features and goals of Early Neutral Evaluation; and circumstances which provide the possibility for the referral of Early Neutral Evaluation in supporting the process of mediating complex civil cases in the civil courts of Malaysia. This study found that, subject to certain modification of the Rules of Court 2012, Early Neutral Evaluation can be used as a viable mechanism to resolve complex civil cases. The referral to Early Neutral Evaluation also improves and supports the understanding of the disputants on the issues surrounding complex cases in a follow-up mediation session. It is expected that this study will contribute significantly to developing a model for resolving complex civil cases involving the referral of Early Neutral Evaluation and mediation in Malaysia.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".