BUSINESS JUDGEMENT RULE: KETENTUAN DAN PELAKSANAANNYA OLEH PENGADILAN DI INGGRIS, KANADA DAN INDONESIA
Bibliographic record
Abstract
AbstractThis article discusses the provisions of business judgment rule (BJR) in the company law and the application of BJR by the courts in the United Kingdom (UK), Canada and Indonesia. In the UK and Canada, the courts have been long examined the appropriateness of directors’ business decisions. Later, BJR was codified into the Canadian Business Corporations Act 2019, meanwhile, duty of care and fiduciary duties were codified into the UK 2006 Companies Law which implicitly regulates BJR. Indonesia adopts BJR in the Company Act 2007 but the courts rarely examine directors’ business decisions and the adoption needs to be rearranged systematically.IntisariArtikel ini membahas bagaimana dan kapan pengadilan menguji aturan penilaian bisnis (APS) dan bagaimana APS diatur dalam hukum perusahaan di Inggris Raya, Kanada, dan Indonesia. Pada pengadilan Inggris dan Kanada yang menganut tradisi hukum kebiasaan, APS telah lama diterapkan untuk menilai keputusan bisnis direktur. Baru-baru ini, APS dikodifikasikan ke dalam Undang-Undang Perusahaan Bisnis 2019. Sementara itu, tugas direktur untuk peduli dan tugas fidusia juga dikodifikasikan ke dalam Undang-Undang Perusahaan Inggris 2006 yang secara implisit mengatur APS. Indonesia juga mengadopsi APS dalam Undang-Undang PT 2007 tetapi pengadilan jarang menguji keputusan bisnis direktur dan adopsi ini perlu diatur ulang secara lebih sistematis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".