A consideration of the mass resignation of directors and their duty to act in the best interests of the company: lessons for Zimbabwe
Bibliographic record
Abstract
This dissertation provides a compelling perspective on Zimbabwean company law, particularly corporate governance. It delves into the duties of company directors to act in the company's best interests when it comes to mass resignation and/or removal. Overall, the literature reviewed in this dissertation identifies the legal gap in the directors’ duties to act in the best interests of the company during mass resignation, the need to acknowledge the possibility of mass resignations, and ultimately, the need for policymakers to establish a regulatory framework for mass resignation and/or removal of directors in Zimbabwe. The dissertation starts with a historical exploration of the regulation of directors' duties in Zimbabwe before focusing on the current legislative framework under the Companies and Other Business Entities Act [Chapter 24:31] 4 of 2019, which partially codifies the directors' duties under consideration. Both the historical and contemporary analyses point to a legal gap in the regulation of directors' duties during mass resignations in Zimbabwe. The research reveals how jurisdictions such as Canada and India have dealt with the issue of mass resignation and/or removal of directors in order to better safeguard the company's and stakeholders' interests. The dissertation demonstrates how important it is to have a regulatory framework in place to safeguard companies’ interests and promote investor confidence among other things. The dissertation concludes with some recommendations for amending the current Companies and Other Business Entities Act [Chapter 24:31] 4 of 2019 to provide for the regulation of directors’ duties during mass resignations and/or removals of directors. It also provides conclusions and lessons that Zimbabwe can learn from the comparator jurisdictions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".