Gender Diversity in the Boardroom and Firms' Effectiveness: A Comparative Analysis Between Nigerian and Canadian Corporate Governance Framework
Bibliographic record
Abstract
Previous study has shown that the presence of three or more women in the boardroom is positively correlated with factors such as stronger organizational health, better decision making and greater diversity of thoughts. Board Gender Diversity (BGD) is not adequately present in Nigeria due to the inadequacy of the corporate governance framework in the country and the cultural and patriarchal nature of Nigeria. The existing research on BGD in Nigeria is extant and there are dearth recent materials on this subject. The limited nature of literature and the sudden disappearance of this subject in Nigeria is the reason for this research. My research aims to contribute to literature by comparison of BGD within the Corporate Governance framework of Nigeria and Canada and providing an investigation into the relationship between BGD and firms’ effectiveness in these two jurisdictions. The study would also highlight key recommendations derived from the comparative analysis and in particular the lessons both countries can learn from each other in relation to BGD and the steps Nigeria can take to achieve BGD.\nThis study shall examine the existing corporate governance codes and framework in Nigeria and Canada as well as its legislative history. It shall also examine and critique the written work of other authors. This research would further employ a comparative methodology to compare and contrast the corporate governance codes and framework in Nigeria and Canada and provide recommendations in a bid to answer the research question posed – Is BGD achievable in Nigeria?
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".