Online dispute resolution: A Ferrari pulled by donkeys?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The heading of the chapter proposes a disconnect between technology and available resources and, thus, anticipates the conclusion that is reached, namely that South Africa’s notorious socio-economic, service delivery and governance problems should be a warning against the hasty adoption of advanced online litigation systems found in other jurisdictions. A more pragmatic solution of incremental reform, rather than revolutionary reform, is suggested. The chapter discusses the compatibility of technology-based reform seen in the context of the normative constitutional values in South Africa, more particularly the right of access to justice. The starting point is to note the response of South African courts to COVID-19 lockdown restrictions. Next, consideration is given to changes in the South African litigation landscape pre-dating the pandemic, which evidence a willingness to step away from the traditional adversarial mind-set and, thus, lays the basis for reform of this country’s civil litigation structures. Reforms predating the pandemic include CaseLines, court-annexed alternative dispute resolution (ADR), and the commercial court. With this base line established, consideration is given to what lies ahead, by referring to reforms found in other more progressive jurisdictions, such as the systems of online dispute resolution that are operational in England, Canada and Utah.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it