Bibliographic record
Abstract
A PDF version of this book is available for free in open access via www.tandfebooks.com as well as the OAPEN Library platform, www.oapen.org. It has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 3.0 license and is part of the OAPEN-UK research project. E-commerce offers immense challenges to traditional dispute resolution methods, as it entails parties often located in different parts of the world making contracts with each other at the click of a mouse. The use of traditional litigation for disputes arising in this forum is often inconvenient, impractical, time-consuming and expensive due to the low value of the transactions and the physical distance between the parties. Thus modern legal systems face a crucial choice: either to adopt traditional dispute resolution methods that have served the legal systems well for hundreds of years or to find new methods which are better suited to a world not anchored in territorial borders. Online Dispute Resolution (ODR), originally an off-shoot of Alternative Dispute Resolution (ADR), takes advantage of the speed and convenience of the Internet, becoming the best, and often the only option for enhancing consumer redress and strengthening their trust in e-commerce. This book provides an in-depth account of the potential of ODR for European consumers, offering a comprehensive and up to date analysis of the development of ODR. It considers the current expansion of ODR and evaluates the challenges posed in its growth. The book proposes the creation of legal standards to close the gap between the potential of ODR services and their actual use, arguing that ODR, if it is to realise its full potential in the resolution of e-commerce disputes and in the enforcement of consumer rights, must be grounded firmly on a European regulatory model.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".