Implementation of the Doctrine of Good Faith (Bona Fides) in Corporate Legal Relations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Good faith (bona fides) is presented in the Civil Code of the Russian Federation as a general principle and presumption. In resolving corporate disputes, the courts are governed by general principles of good faith. However, corporate relations have a specificity due to, inter alia, the variety of corporate forms. It can be assumed that the application of good faith provisions should also vary taking into account the characteristics of corporate patterns, the types and forms of corporate relations, subjective internal corporate circumstances. Common law countries have developed a system of good faith elements and special tests to apply the required requirement of good faith according to the context. A special place is given to fiduciary relations as a product of bona fides. The author has carried out a comparative analysis of the provisions of the Plenums of the Supreme Court of the Russian Federation, the Supreme Court of the Russian Federation and the law enforcement practice of Germany, the USA, Great Britain and Canada on the issues of good faith in the consideration of corporate disputes. Special attention is paid to the interrelation between corporate ethics and law. Examining a number of key cases from the law-enforcement practice of the courts of the Anglo-American system of law, the author substantiates the possibility of applying special tests, namely, objective and subjective good faith tests, to regulate matters related to the application of the rules of good faith from the Civil Code and special laws in dealing with corporate disputes. Special attention is paid to the role of courts and permissible discretion in the formation of standards of enforcement of blanket norms and general principles of law in corporate relations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it