UNTANGLING THE REQUIREMENT OF GOOD FAITH IN THE DERIVATIVE ACTION IN COMPANY LAW (Part 2)
Bibliographic record
Abstract
A crucial prerequisite for a derivative action is that the applicant must be acting in good faith in terms of section 165(5)(b)(i) of the Companies Act 71 of 2008 in order to obtain the leave of the court to bring the proposed derivative action. Both the Supreme Court of Appeal and the High Court have recently made important pronouncements of legal principle on the approach that the courts would take to the determination of good faith for the purposes of the statutory derivative action under section 165 of the Companies Act. These judicial findings relate not only to the complex issue of how to prove good faith but also to the meaning and content of the requirement of good faith. The courts have now reached a crossroads in delineating the content of good faith and how it is to be proved. This two-part series of articles critically evaluates these judicial pronouncements. While the focus of these articles is mainly on the tangled requirement of good faith, relevant judicial findings on the other prerequisites for a derivative action under section 165(5)(b) read with (7) and (8) of the Companies Act are also discussed. A comparative approach is adopted which takes into account the jurisprudence developed in Australia, Canada and Singapore. The first article in this series of two articles discussed the test of good faith. This article focuses on the proof of good faith.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".