Business Judgment Rule: Defense for the Directors in Cases of Alleged Breach of Duties
Bibliographic record
Abstract
In this article researchers will explain about Business Judgment Rule and how it can be used as a defence by the directors’ of the corporation. In simple language it can be said that the “Business judgment rule” is nothing but a judicially evolved doctrine derived out of case laws in the field of corporate laws. This doctrine has its origin in USA followed by U.K. The rule is in use in some form or the other in the common law countries e.g. whales, Australia, Canada, India &c. Australia has codified this rule under sec.1180(2) Corporations Act 2001, in South Africa Companies Act 71 of 200 section 76(4) provides for director’s duty to work towards best interest of the business with due care, skill and diligence, in India section 166(2) of Companies Act, 2013 requires that for the benefit of different constituencies of a company a director must act bona fide to promote the object of the company. The Business Judgment Rule tries to protect the directors of the company by creating a safe harbour for those who works for the betterment and interest of the corporations in an honest manner and in good faith. The scope of the paper is restricted to mainly US decisions, which has seen the greatest development in interpreting cases, though certain important landmarks in the Indian and UK context have also been referred to. The paper is limited by secondary sources such as books, articles and reports available on the subject.
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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 | 0.031 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".