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Using the Trust Management Mechanism as a Way to Prevent Conflicts of Interest in the Public Service: the Experience of Canada, Chile and Albania

2022· article· en· W4294597644 on OpenAlexaboutno aff
Svetlana Sergeevna Gorokhova

Bibliographic record

VenueЮридические исследования · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Issues
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationLegislatureEnforcementIndependence (probability theory)Relevance (law)Service (business)Political scienceInstitutionVirtueLaw and economicsPublic administrationLawBusinessSociology

Abstract

fetched live from OpenAlex

The subject of the study is the legislative and law enforcement experience of countries such as Canada, Chile and Albania in the use of different forms of trust management of property of civil servants and officials as a tool to overcome conflicts of interest in the civil service. The relevance of this study is confirmed by the fact that, on an equal footing with the United States, these states are among the few using this tool, as is the Russian Federation. However, the domestic legal regulation of this institution is still not perfect enough, therefore, it is important to study the experience of those states where there is such a practice. The scientific novelty of the research is determined by the fact that at present there are practically no works containing an analysis of the institute in question. In the course of the study, the following conclusion was made: What is common to the legislation of all the countries considered is that each of these states strives, by virtue of its capabilities, to free the actions of the trustee as much as possible from the influence of the founder of the trust management on him, that is, to ensure the independence of the former from the latter on the management of the entrusted property. However, as the researchers note, even in the most advanced and strict variants, it is hardly possible to avoid the interaction of stakeholders completely. Nevertheless, at least formally, all regulations concerning this issue establish a rule according to which the trustee should not be affiliated with the principal through any channels. This can be applied quite easily in Russian legislation.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.103
GPT teacher head0.344
Teacher spread0.241 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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