Conflict Resolution with Equitative Algorithms: A Tool to Establish A European Common Ground of Available Rights
Bibliographic record
Abstract
The current study examines the application of algorithms in resolving civil conflicts within the EU with specific focus on divorce and inheritance concerning asset division. For that purpose, this paper initially argues the applicability and advantages of deploying algorithmic conflict resolution for civil disputes, in general terms. Then, the best practices established at the global level in the United States, Canada and Australia will be discussed followed by the European approach towards the use of algorithms in resolving disputes. Next, the authors will focus on arguing how the use of the algorithmic dispute resolution method can best fit within the European context of civil dispute resolution-considering the existing inconsistencies among civil and civil procedural rules of the Member States-leading us to establish for the first time a European Common Ground of Available Rights at the EU level. Finally , this study lays out the project on Conflict Resolution with Equitative Algorithms (CREA) and looks at the results achieved through the data collection process and analysis of such data contributing towards the two major practical achievements of this project, namely developing CREA Software, which assists disputants to resolve their property division related conflicts through this online tool, and the establishment of the EU Common Ground of Available Rights framework, with the principle aim of tackling the existing inconsistencies in civil and civil procedural rules on divorce and inheritance within the EU.
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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 | 0.030 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".