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Record W2971146987 · doi:10.5539/jpl.v12n5p1

Security Measures and Liability Measures in Loan Agreements

2019· article· en· W2971146987 on OpenAlexvenueno aff
Artur Ilfarovich Khabirov, Gulnara Mullanurovna Khamitova

Bibliographic record

VenueJournal of Politics and Law · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Issues
Canadian institutionsnot available
FundersKazan Federal University
KeywordsLegal liabilityCivil codeSafeguardingLoanParagraphPolitical scienceLawLiabilityRussian federationBusinessJoint and several liabilityCode (set theory)Law and economicsEconomicsFinanceComputer science

Abstract

fetched live from OpenAlex

The Civil Code of the Russian Federation regulates the use of various measures to protect violated rights and interests: first, these include universal methods for protecting civil rights (article 12 of the Civil Code); second, these include provisions of Chapter 25 of the Civil Code regarding the liability for violating one's obligations; both of them jointly comprising the institution of protection of civil rights. This article studies the issue of consequences for violating a party's duties under a loan agreement. The article differentiates safeguarding measures and liability measures to be used in case of an offense. The article also makes a conclusion regarding whether such differentiation is appropriate. Based on such differentiation, we analyze Paragraph 1 of Chapter 42 of the Civil Code of the Russian Federation.

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)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0050.015
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.329
Teacher spread0.300 · 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 designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

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