THE INTERNET AS A SITE OF LEGAL EDUCATION AND COLLABORATION ACROSS CONTINENTS AND TIME ZONES: USING ONLINE DISPUTE RESOLUTION AS A TOOL FOR STUDENT LEARNING
Bibliographic record
Abstract
Increasingly, digital technologies are influencing and impacting dispute resolution, particularly in the emerging field of online dispute resolution (ODR). ODR holds the potential to increase access to justice by engaging disputants in dramatically new ways. As a relatively new subject, ODR is unlikely to form part of the traditional curriculum at law schools. Aside from the question of whether it will become a mainstream part of tomorrow’s legal or dispute resolution landscape, ODR does show us that a familiarity with technology is becoming more important for tomorrow’s lawyers. As educators, how can we expose law students to these new forces of change in a meaningful way? How can we help students understand the benefits and drawbacks technology holds for the challenge of access to justice? This article describes a unique pilot project of an ODR simulation involving three universities in three cities, two continents, and three time zones. The main objectives of the project were to expose law students to ODR from the perspective of a disputant or client; expose clinical mediation students to a range of technology-based dispute resolution processes; demonstrate the potential for technology to support collaboration across vast distances; and promote experiential education by giving students “hands-on” ODR experience. This article will describe the simulation from an educator’s perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".