The effectiveness of the internal corporate governance mechanism and the ownership of the government and agencies
Bibliographic record
Abstract
This paper examines the impact of the government and its agencies’ ownership on the effectiveness of one the main internal governance mechanisms, namely; board of directors, for a sample of 140 energy and petrochemical Saudi listed firms over 2012-2019. The Saudi Arabia provides an interesting context due to the domination of government-linked corporations’ ownership. This setting arranges for the impact of such ownership on the board of directors’ monitoring and advisory roles. The board of directors’ effectiveness is measured as an interaction term of the board size and meetings of the board of directors. The study finds that government-linked energy and petrochemical corporations’ ownerships are inversely related to the board of directors’ effectiveness. This result is sensitive to the measurement of the board of directors’ effectiveness as each variable consisting of the board of directors’ effectiveness was examined individually. The study also finds that government-linked corporations’ ownership had a strong negative impact on the board size. In contrast, the proposed model does not provide any evidence supporting the relationship of the government-linked corporations’ ownerships with board meetings. Overall, the evidence supports the substitution hypothesis on the relationship of government-linked corporations and board of directors’ effectiveness.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".