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Record W3171145257 · doi:10.6000/1929-4409.2021.10.52

Economic Law and Standardization: A Basis for Avoiding Risks in Business

2021· article· en· W3171145257 on OpenAlexvenueno aff
Tetiana Popovych, Oleksandr V. Bezukh, Hryhoriy I. Trofanchuk, Tetiana B. Pozhodzhuk

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsStandardizationLegislationConformity assessmentConformityStatutory lawUkrainianBusinessLawRelevance (law)Scope (computer science)AuditLaw and economicsPolitical scienceAccountingEconomicsComputer scienceOperations management

Abstract

fetched live from OpenAlex

Currently, in Ukraine, there is a system of technical regulation as part of the general system of standardization. Technical regulation is defined as a means of state regulation, which, like any legal regulation, is implemented by appropriate legal means. This explains the relevance of this study. This paper investigates the Economic Code of Ukraine, several Ukrainian laws (the Law of Ukraine "On Environmental Audit", the Law of Ukraine "On Standardization", the Law of Ukraine "On Technical Regulations and Conformity Assessment", etc.), and State standards. Technical regulation was also considered as a general category and a legal phenomenon, as a result of which it was noted that the technical regulation adopted in Ukraine for dividing products into food and non-food products is only a matter of supervision over the conformity of goods and the use of conditions for a specific legal act and type of product. It was concluded that standardization proceeds from social regulation and generates norms of a technical, organizational, or other orderly nature, transforming into legal provisions. Therewith, technical regulation also derives from legal regulation and gives rise to technical guidelines, which constitute statutory regulations that form part of the national legislation of Ukraine, including economic legislation. As a result, it is proposed to improve and supplement the wording of Part 2 Article 16 of the Law of Ukraine "On Standardization", and it is also proposed to reword Article 24 of the Law of Ukraine "On Technical Regulations and Conformity Assessment". Keywords: Technical regulation, laws of Ukraine, state standards, ISO, regulations.

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)0.020
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0070.058
Scholarly communication0.0150.016
Open science0.0020.007
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.105
GPT teacher head0.329
Teacher spread0.224 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Criminology and SociologySame topicEconomic Issues in UkraineFrench-language works237,207