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LEGAL REGULATION OF THE USE OF FOREST RESOURCES IN RUSSIA FOR CHEMICAL INDUSTRY IN THE SECOND HALF OF THE XVII — THE FIRST QUARTER OF THE XVIII CENTURIES

2022· article· en· W6888756840 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Regulatory Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)NormativeState (computer science)Production (economics)Relevance (law)Forest industry

Abstract

fetched live from OpenAlex

In the history of any state, the forest domain played a great role. Russia, being a country with the richest forest resources, pays great attention to the state regulation of forest management and forest conservation. The history of the use of forest resources is part of the history of Russia. The relevance of the topic is due to the change in the concept of teaching the history of Russia for students studying in specialized historical and non-core specialties and areas of training in universities. The number of hours devoted to the study of national history is increasing; the history of Russia will be singled out from the course of world history as an independent academic discipline. Students will have the opportunity to study in more detail various aspects of the history of the Russian state. The article analyzes the measures of state regulation of the use of forest resources for the production of potash, resin and tar in Russia in the 17th century. — the first quarter of the XVIII century. and also questions of regulation of trade in the specified goods. The analysis of normative legal acts that determined the conditions and features of production and trade in products obtained from wood processing was carried out. It is concluded that the state’s focus only on administrative and legal measures to regulate the use of forests in Russia, without taking into account economic realities, demonstrates low efficiency and leads to crisis phenomena in the area under study.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.138
GPT teacher head0.446
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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