MétaCan
Menu
Back to cohort
Record W4212909218 · doi:10.34069/ai/2022.49.01.16

The analysis of the implementation of inheritance law in selected EU countries

2022· article· en· W4212909218 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRevista Amazonia Investiga · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Studies and Reforms
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsInheritance (genetic algorithm)LegislationDialecticSubject (documents)OriginalityLawValue (mathematics)Political scienceGeneralizationEpistemologySociologyComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

The purpose of the article is to analyze the peculiarities of the inheritance procedure of individual European countries. The subject of the study is the implementation of inheritance law in Spain, Germany and Austria. The research methodology includes the use of general scientific and special methods of scientific cognition: dialectical, historical and legal, formal and logical, method of hermeneutics, generalization, comparison, etc. Research results. The procedures for implementation of the right to inheritance in Spain, Germany and Austria are considered. The forms and features of making wills in these countries are studied. The cases of acceptance and rejection of inheritance are analyzed. The right of minors to make a will is covered.. The practical implication lies in the possibility of applying international norms in the legislation of Ukraine. Value / originality. The Authors’ proposals on the implementation of European experience in the inheritance legislation of Ukraine are given.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
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 head0.013
GPT teacher head0.296
Teacher spread0.284 · 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