Bibliographic record
Abstract
Abstract This book contains the most detailed multi-jurisdictional analysis of directors’ conflicts available drawing together relevant case law, codes and statutory regulation from the law applying to directors of companies incorporated under the UK Companies Acts, with extensive reference to the law in Australia, Canada, Hong Kong and New Zealand. The book provides comprehensive analysis of the conflicts faced by directors and includes the important areas of conflicts of interest, conflicts of duties, unauthorised profits, corporate opportunities, multiple directorships, nominee directorships, and conflicts involving stakeholders’ interests. Difficult aspects of these topics are analysed with reference to the laws of a range of common law jurisdictions. The extensive multi-jurisdictional analysis allows solutions to be presented in relation to difficult legal issues and enables clarification of the legal approach. In addition to detailed coverage and analysis of general law duties, the specific statutory duties are outlined and analysed including those concerning related party transactions. The UK Corporate Governance Code, and Guidance on Board Effectiveness, issued by the FRC in July 2018 are covered extensively. The book provides detail on fiduciary theory, the reach of the term ‘director’, consequences of a breach, remedies, authorisation and the role of disclosure. It also contains a detailed table of key cases concerning corporate opportunities which includes the pertinent facts, whether there was a breach of directors’ duties, and a summary of the important factors in the decision made. The cases are featured in order from instances representing clear breach to those in which no breach was found. The book is significant in its thorough coverage of general law and statutory duties relating to conflicts, and its clarification of the scope and application of currently complex and uncertain duties. It provides clear guidance to academics, practitioners, directors and regulators in each of the jurisdictions on the regulation of conflicts of interest and the implementation of good regulatory practice. This is a key reference work on this important and dynamic area of company law which provides careful analysis of the law set in a practical context.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".