The development of e-mediation in Europe and in the rest of the world: comparative approach and the reasons for a double speed
Bibliographic record
Abstract
The principal objective of the current study is to thoroughly examine the role of e-mediation and online dispute resolution as valuable tools for resolving legal disputes in civil and commercial matters and the reasons why this remedy is growing at a slower speed in Europe than in the rest of the world. To this end, this paper gives an overview of the concept of ‘mediation’ in the context of alternative dispute resolutions (ADRs) and its status in the context of e-mediation in general. Then, this study provides an overview of the historical origin and worldwide practices concerning e-mediation to understand the reasons behind its greater development in the countries where it originated: U.S.A., Canada, Australia and the UK. The work finally briefly analyses the perspectives for the development of e-mediation in Europe and in the rest of the world. The reason for the two rates of development of those systems is to be identified in the specific worry that European systems have about balancing the privatisation of justice with the guarantee of fundamental rights, especially concerning an effective access to justice.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".