Corporate Governance and the Earnings Quality of Nigerian Firms
Bibliographic record
Abstract
The focus of the study is to examine the impact of corporate governance on earnings quality in listed firms in Nigeria. The specific objective is to investigate the effect of board size, board independence and board gender diversity on earnings quality. This study was carried out with secondary data retrieved from corporate annual reports of the sampled companies and the data was analysed using panel regression on a sample of 37 quoted manufacturing companies for the period 2011-2017. On the overall, the result reveals that Board size, board independence and board gender diversity used for measuring corporate governance show significant impact on earnings quality. In addition, corporate governance variables appear to be quite sensitive to the measure of earnings quality used. Based on the findings, the study recommends the need for comprehensive evaluation of corporate governance systems of companies. The study recommends the need for more level of board independence. The diversity issue though is gaining momentum in corporate governance literature can still be regarded as not as dominant as compared to others especially as it relates to protecting shareholder rights and framing dividend policy. The significance of the variable nevertheless suggests that companies should thrive to achieve an appropriate diversity mix.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".