Classification of Legal Entities of the Russian Empire in the Period from the 18th Century up to the First Quarter of the 19th Century
Bibliographic record
Abstract
Предметом настоящего исследования являются виды юридических лиц Российской империи в период с появления первых отечественных законов, регулирующих их деятельность, до появления закона об акционерных компаниях 1836 года. Автор обозначает основные нормативные акты Российской империи XVIII – начала XIX века о юридических лицах, а также анализирует статус появляющихся в Петровскую эпоху организаций и учреждений, выстраивая их систему. Практическое значение исследования заключается в том, что в условиях современного реформирования корпоративного законодательства выводы автора могут быть использованы при изучении дисциплины гражданского права и истории права. The subject of this research is the types of legal entities of the Russian Empire in the period from the appearance of the first domestic laws regulating their activities to the appearance of the law on joint stock companies in 1836. The author identifies the main normative acts of the Russian Empire of the 18th – early 19th centuries on legal entities and analyzes the status of organizations and institutions that appeared in the Peter the Great era, building their system. The practical significance of the study lies in the fact that in the context of modern reformation of corporate legislation the author’s conclusions can be used in the study of the discipline of civil law and the history of law.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".