Corporate rescue reform in Nigeria : a comparative analysis of the law and policy in the context of African rehabilitation models
Bibliographic record
Abstract
This thesis critically examines the corporate rescue reform in Nigeria, focusing on the extent to which the Companies and Allied Matters Act (CAMA) 2020 facilitates the effective and efficient rescue of financially distressed companies. Historically, Nigeria’s insolvency law significantly focused on creditor recovery, resulting in the winding up of companies in financial distress, which increased business closures, loss of jobs, and economic uncertainty. This traditional approach to insolvency, entrenched under the CAMA 1990, provided limited corporate rescue options. Hence, the CAMA 2020 was enacted, in part, to shift the focus of Nigeria’s insolvency law from liquidation and winding-up to the rescue of companies with the introduction of modern corporate rescue tools such as the Companies Voluntary Arrangement and Administration. Taking a comparative and analytical approach, the thesis interrogates the corporate rescue models from leading Anglo-American jurisdictions, such as the United Kingdom and the United States, and the African jurisdictions, such as Kenya and South Africa, to provide a basis for assessing CAMA 2020. It demonstrates that CAMA 2020 incorporates the hallmarks of an effective and efficient insolvency system based on the benchmark under the UNCITRAL Legislative Guide. However, it argues that the attempt to shift the focus of Nigeria’s insolvency law has not altered the statistics on corporate failure due to practical challenges that limit the application of CAMA 2020. These challenges are particularly evident in the lack of a cross-border insolvency framework and weak institutional capacity. By benchmarking Nigeria’s corporate rescue model with the models from select African jurisdictions, Nigeria can draw lessons to strengthen its corporate rescue policy. The thesis, therefore, highlights the need for substantial alignment with international best practices, including the need to strengthen relevant institutions to support more efficient implementation of the corporate rescue tools under CAMA 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".