Bibliographic record
Abstract
This Essay stems from my role as a commenter for a panel discussion among leading thinkers on the topic of online dispute resolution (wODRx). 1 Generally, ODR utilizes information and communication technology to prevent, manage, and resolve disputes.The conference served as a timely pause to assess what ODR is, how it fills diverse functions in the dispute resolution field, and when it can better meet the needs of parties and increase the accessibility and transparency of dispute resolution. 2 The panelists highlighted their study of consumer, commercial, and judicial ODR.As a follow-up, this Essay compares examples offered by the panelists through the lens of dispute system design: the study of the process and product of resolving disputes of a specific category.ODR emerged from the unique needs of online e-commerce where it was geographically and legally infeasible to bring disputes to court for resolution.3 In this global online marketplace, eBay was the first to use ODR, building a private online option to address disputes arising from transactions conducted through the site.4 Since that time, ODR platforms have unfolded in both private and public domains.Now, nearly fifty courts in the United Statesvas well as courts in Canada, the Netherlands, India, Brazil, the United Kingdom, and Chinavhave established ODR process options.ODR potentially enables efficiency through processes that are faster and cheaperva difference in degree relative to traditional, face-to-face processes.My modest experience as an online mediator suggests a difference in kind, as well in the qualities of online processes.For example, users experience a difference in the use of various communication channelsvsynchronous versus asynchronous and textual versus visual, respectively, relative to the synchronous, visual communication of face-to-face dispute resolution.5 The experience of online dispute handling may feel foreign to some who prefer person-to-person contact, while the opposite may be true for those who have been online since childhood.
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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 | 0.048 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.015 |
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".