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.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".