Good Practices in Corporate Governance: One-Size-Fits-All vs. Comply-or-Explain
Bibliographic record
Abstract
There are many studies exploring the effects of hard and soft legislation on good practices. Few studies examine the strengths and weaknesses of particular legislation. These mainly focus on positive results of legislation and justify the application of existing approach for better practices.The study compares the approaches of hard and soft legislation. It finds that both approaches are suitable for implementation despite of state policy. The analysis is developed by using the practices of several countries for latest 20 years.This study explored national legislation and its effects on good practices in corporate governance. It revealed that there was no significant difference between state policy and approaches, and between legislation and corporate governance practices.The objective of the study is to develop deeper insight into pros and cons adoptation of approaches. Comparative analysis shows that state policy determines the good practices. Moreover, the conclusion point out that there is a small freedom of action for companies to attract investors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".