LEGAL REGULATION OF FISHERY IN RUSSIA AND FOREIGN STATES: COMMON AND SPECIAL FEATURES
Bibliographic record
Abstract
The aim of the article is a comparative analyze of legal regulation of fishery in Russia and foreign states. According to this goal, the task of comparison of fishery legislation of Russia and other CIS countries, Russia and EU State as well as North American countries arises. The author tries to solve one more issue — to consider how current legal regulation correlates with the concept of fishery in Russia. Legislation in the sphere of fishery is developing dynamically. Despite the different time of appearance and the differences of legal systems, mentioned legislation can be compared in the basic directions of legal regulation. The article applied the method of comparative legal regulation, the historical method of extrapolating. Also the scientific methods of deduction and induction are applied. The author used the instruments of international soft law and the works of specialists in the mentioned area of the legal regulation. The article consists of following sections: statement of the question; sources of law; key rules-definitions; principles and key provisions of the legislation; the quota system of catches, licensing and contractual methods of regulation; the payment principle and the nature of the distribution of funds received. In conclusion the author emphasized the approximation of the legislation of Russia and other CIS countries in the field of fisheries (concerning the concepts, basic provisions, types of fishery and quotas). Also, there are some similarities in a combination of licensing and contractual methods of regulation in Russian and Canadian legislation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".