An International Review of Early Neutral Evaluation Programs and Their Use in Family Law Disputes in Alberta
Bibliographic record
Abstract
Early neutral evaluation (ENE) is an expedited dispute resolution process that was developed in California 20 years ago (Brazil, 1990; Brazil, Kahn, Newman & Gold, 1986). Despite very positive program evaluations in the United States (Kakalik et al., 1996; Levine, 1989a; Pearson, 2006), its adoption in other jurisdictions has been somewhat limited. Recently, and likely in response to the need to improve access to justice in family law matters in an efficient and economic manner, ENE programs have been gaining in popularity; examples of ENE programs can now be found in other countries including the United Kingdom, Australia, New Zealand, Malaysia, and Singapore. Given their limited use in Canada, the Canadian Research Institute for Law and the Family, with financial support from the Alberta Law Foundation, conducted this project to review and analyze early neutral evaluation processes in other jurisdictions and explore their possible utility for Alberta. The purpose of this project was to review and analyze early neutral evaluation processes elsewhere and make recommendations regarding their possible use in Alberta to improve access to justice in family law disputes. Specifically, this project had the following objectives: to review the current literature on early neutral evaluation processes in Canada and elsewhere; to analyze early neutral evaluation models and identify the advantages and disadvantages of model characteristics; and to make recommendations regarding best practices for early neutral evaluation processes in family law matters in Alberta. This project involved an international literature review of ENE processes. The literature review was conducted using both academic databases and online search engines, yielding a combination of academic (published) and gray (informally published) material. Research studies were reviewed to examine emerging trends, issues, and best practices in ENE models in family law matters, and particular attention was given to programs that had been evaluated.
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".