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Record W2992654381 · doi:10.32575/ppb.2019.2.4

A magyar bizalmi vagyonkezelés egyes külföldi országok szabályozásainak tükrében

2019· article· hu· W2992654381 on OpenAlexaboutno aff
István Sándor

Bibliographic record

VenuePro Publico Bono - Magyar Közigazgatás · 2019
Typearticle
Languagehu
FieldSocial Sciences
TopicHungarian Social, Economic and Educational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeorgianCzechRomanianGermanCivil codePolitical scienceInstitutionLawGeography

Abstract

fetched live from OpenAlex

At the end of the 20th century, several Eastern European countries showed strong interest in the Anglo–Saxon institution of the trust. The main reason for this is attributable to the advantages of the trust offered for the economy. Legislators in some Eastern European countries tried to cope with problems relating to the adoption of a trust-like legal institution. As the result of their efforts, the civil code of Russia (1995/96), Lithuania (1996/2000), Georgia (2002), Romania (2011), the Czech Republic (2012) and Hungary contains regulations that resemble the trust. These laws vary significantly because the Georgian model remains on a contractual level along with the Russian law. In Lithuania it is possible to establish a so-called right of trust, which is an independent item in rem right. The Romanian regulation is very similar to the French fiducie, while the Czech trust law was worked out on the basis of the Québec model. The Hungarian regulation was drawn up on the basis of the model of the trust in English law and that of the Treuhand in German law.

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 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.478
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.024

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 head0.026
GPT teacher head0.290
Teacher spread0.265 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

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