MétaCan
Menu
Back to cohort
Record W2971146987 · doi:10.5539/jpl.v12n5p1

Security Measures and Liability Measures in Loan Agreements

2019· article· en· W2971146987 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
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