Pecuniary interests and the rule against adjudicative bias: The automatic disqualification or objective reasonable approach?
Bibliographic record
Abstract
This article deals with the issue of bias arising from pecuniary interest of a judge. Essentially, it asks the question: when does the pecuniary interest of a judge diminish his/her ability to apply his/her mind impartially to the dispute before him/her. To answer this question, the article undertakes a synthesis of the various rules and tests applied across Commonwealth jurisdictions and then compares them with the South African approach as outlined in two recent cases, namely Bernert v ABSA Bank Ltd 2011 (3) SA 92 (CC) and Ndimeni v Meeg Bank Ltd (Bank of Transkei) 2011 (1) SA 560 (SCA). Broadly, the article discusses the key aspects of the automatic disqualification approach preferred by the English courts, the Canadian objective reasonable approach and the realistic possibility approach recently adopted by the Australian courts. The article concludes that the South African approach that places emphasis on the objective reasonable test, complemented by the realistic possibility approach, may be most suitable, given the nature of complaints so far dealt with by the courts and the full propriety of the injunction in section 34 of the Constitution.
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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 | 0.044 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".