Bibliographic record
Abstract
This paper offers integrated theoretical-based empirical evidence regarding the role of female directors in promoting corporate common good using resource dependency model built on critical mass hypothesis. Using panel regression involving 220 firm-year observations from 2011 to 2021, the paper empirically assesses the moderating impacts of diversity and social inclusion policy, and gender-based power separation in determining the direction of causality between composition of female directors and foreign capital importation by the top 20 commercial banks in Nigeria. With approximately 30 per cent female director representation in the sampled banks (i.e., optimal gender threshold), the paper offers support to critical mass hypothesis and validates intrinsic benefits of women in corporate boardroom. The empirical result shows that female director representation in boards with strong diversity and social inclusion policy and greater independent non-executive directors, is positively linked to resource dependency role of foreign capital importation. Diversity of power separation is found to be detrimental to such board tasks due to overwhelming tokenism effect that surrounds gender-based power delineation. These key findings are statistically significant and robust to a series of iterated sensitivity tests. In addition to offering emerging market contributions to the growing literature on critical mass theory application, findings from this study demonstrate the inherent value of combining multiple governance theories (such as resource dependency and critical mass models) and the dynamic research framework opportunities it offers for robust empirical testing.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".