Peculiarities of Corporate Governance Methodology
Bibliographic record
Abstract
This work is devoted to the study of the quality and identification of priorities of institutional investors in corporate governance in Russia. The authors conclude that the main task of the corporate governance system is to create favorable conditions for broad attraction of foreign investments in Russian companies and to increase the role of shareholders in strategic management. At present, the companies from developed countries achieve higher economic performance through the introduction of modern methods of corporate governance. The adoption of the new Code of Corporate Governance in the Russian Federation in 2014 will create an opportunity to improve the efficiency of Russian companies and to conduct their activities in accordance with international standards through the introduction of modern corporate governance practices, including ensuring transparency of activities for investors. At the same time, despite significant improvements in corporate governance practices of Russian companies, the level of its quality in comparison with foreign countries remains low. The article highlights both positive and negative trends in the Russian practice of corporate governance. One of the effective corporate governance methods is to ensure feedback between Russian companies and institutional investors. The article presents the results of a survey of opinions of investors that allow to identify the main factors of corporate governance that motivate them to take investment decisions on the Russian market.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".