Small Claims and the Pursuit of (Digital) Justice: A Tiered Online Dispute Resolution Perspective
Bibliographic record
Abstract
This paper investigates the most recent developments in completely online small claims processes as a response to the extreme delays in delivering justice by courts. This study argues that adopting a tiered online dispute resolution (ODR) system design can increase access to justice for individuals by simplifying the processes; reducing excessive procedural length and costs; also expanding accessibility to dispute resolution bodies. The present research also proposes that the COVID-19 pandemic has widely opened a bundle of opportunities for complete digitalisation of small claims procedures at the EU and Member State levels. Nevertheless, it deems necessary to closely monitor the function of these systems to ensure that the digitalised small claims procedures meet the standards of procedural fairness and efficiency of justice, in particular concerning self-represented litigants. Thus, the overall structure of this paper takes the form of four sections. The first part lays out the evolution of ODR in relation to small claims and analysing a tiered ODR system design for these cases. Section II gives an overview of the most prominent operating online small claims processes from a global perspective in the United Kingdom, Canada, China, and the United States. The third section is concerned with the status of online small claims processes and the taken measures at EU and Member States level. The final part provides a discussion on the lessons learnt, the opportunities, and the risks in full digitalisation of small claims processes.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| 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".