Bibliographic record
Abstract
If dissenting judgments perform a valuable function in the administration of justice, too little dissent may indicate that the administration of justice is not reaping the benefits of dissent. South Africa belongs to the common-law tradition, which has always allowed dissenting judgments. The civil-law system traditionally did not, and this is still the position in many countries. In the modern era, considerations of transparency and accountability favour the disclosure and publication of dissenting judgments. Although they can play a role in the development of the law, their most valuable function is to improve the quality of judicial output by requiring majority judgments to confront the dissenting judgments’ reasoning. Factors which may affect the extent of dissent in appellate courts include case complexity and control over rolls; panel sizes; judicial diversity, personality and turnover; court leadership; research resources; modes of judicial interaction; and protocols on the timeliness of judgments. Data on dissent in South Africa’s Constitutional Court, Supreme Court of Appeal and Labour Appeal Court, as well as in the United Kingdom, Australia, Canada and the United States, suggest that there is less dissent in our intermediate appellate courts than might be expected. Changes in work procedures could yield a healthier pattern.
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 | 0.034 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".