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HISTORY OF THE DEVELOPMENT OF PROPERTY LEASE RELATIONS: CIVIL AND LAND LEGAL DIMENSIONS

2024· article· en· W4407673198 on OpenAlexaboutno aff
B. V. Karapysh

Bibliographic record

VenueScientific Notes Series Law · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsLeaseProperty (philosophy)Development (topology)Property lawLaw and economicsBusinessPolitical scienceSociologyLawEpistemologyMathematicsPhilosophy

Abstract

fetched live from OpenAlex

The article examines the historical aspects of the development of property lease (rent) relations. The peculiarities of the development of property lease as an independent legal category are analyzed. Attention is focused on the specifics of the locatio-conductio contract under the law of Ancient Rome as a prerequisite for modern lease (rent) relations in the field of civil law and land use, respectively. The characteristic features of the retrospective regulation of the lease agreement (lease) under the legislation of Great Britain (period 1550-1700), Germany (period 1870-1940), Canada and Quebec (1985 - present), Galicia (Galician Civil Code of 1797), the Ukrainian SSR (1922-1991) and independent Ukraine (1991 - present) are presented. The features of the regulation of the lease (lease) of land plots under the legislation of Great Britain, Canada, Quebec and Ukraine of the period of independence, respectively, are studied. The conclusion is made that the legal regulation of property lease (lease) relations should be considered comprehensively, taking into account both general civil principles and land-legal features of the lease of a land plot, determined by land legislation. In Ukraine, there are both common and distinct features between the civil and land law doctrines of lease (rent). The Civil Code of Ukraine defines general provisions on lease (rent) as a legal construct, and the specialized legislation – the Land Code of Ukraine, the Law of Ukraine “On Lease of Land” defines the features of lease (rent) of a land plot for commercial and other needs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.009
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.034
GPT teacher head0.252
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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