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QUESTIONS OF LIMITS OF LIMITATION OF SUBJECTIVE RIGHTS IN ABSOLUTE LEGAL RELATIONS: AN INTERSECTORAL APPROACH

2022· article· en· W4389371719 on OpenAlexfundno aff
Albina Kochneva

Bibliographic record

VenueEx Jure · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Issues
Canadian institutionsnot available
FundersMcGill University
KeywordsObligationAbsolute (philosophy)LawPolitical scienceLaw and economicsState (computer science)SociologyMathematicsEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract: the presented article is devoted to the problems of determining the criteria for restrictions of subjective rights realized in absolute legal relations. The author positions the need to search for the “core” of absolute subjective law, defined as an indicator of the inadmissibility of any further restriction of it, through specific legal relations. In this connection, he applies an intersectoral approach to the analysis of the problems of the implementation of absolute legal relations: the branches of family, civil, and constitutional law are studied. The author notes the doctrinal and practical conflict-of-laws aspects of the sanctioned nature of restrictions on the realized paternity rights, patent rights, as well as inalienable constitutional rights in the conditions of an extraordinary regime of legal regulation. In the course of the analysis, relevant materials of domestic judicial practice are used. According to the author, the need for an authorized restriction is a prerequisite for changing the internal nature of a passive legal obligation on the part of obligated subjects in absolute legal relations. The obligation is mediated by state goal-setting and expediency, which cancels the unconditional classical nature of the absolute legal relationship.

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)0.026
metaresearch head score (Gemma)0.045
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: Other · Consensus signal: Other
Teacher disagreement score0.026
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.045
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0070.065
Scholarly communication0.0160.020
Open science0.0030.012
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.043
GPT teacher head0.349
Teacher spread0.305 · 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
GenreOther

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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