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Record W4225144606 · doi:10.18280/ijsse.120207

Influence of COVID-19 on the Functional Device of State Governance of Economic Growth of Countries in the Context of Ensuring Security

2022· article· en· W4225144606 on OpenAlexvenueno aff
Myroslav Kryshtanovych, Liudmyla Antonova, Svitlana Dombrovska, Tetiana Pidlisna

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsIDEF0Context (archaeology)Risk analysis (engineering)PandemicEconomic securityCoronavirus disease 2019 (COVID-19)Corporate governanceEnvironmental economicsManagement scienceProcess managementComputer scienceBusinessEconomicsEconomic growthEngineeringMedicine

Abstract

fetched live from OpenAlex

The purpose of the paper is to analyze the negative impact of pandemic on the economic growth of the world and the choice of strategy for the functioning of the apparatus of public administration using the chosen methodological approach in the context of ensuring security. The research methodology includes both general scientific methods, such as analysis and synthesis to assess the negative impact of pandemic on economic development and growth, and comparison methods to demonstrate growth rates in the context of ensuring security. IDEF0 methodology was also used to present a methodological approach to the implementation of the proposed strategies. Research has several limitations and they are primarily related to the fact that economic growth strategies have been presented for one country. The proposed methodological approach to the implementation of the strategy can be applied in practice in the apparatus of public administration. The value of the study includes the use of IDEF0 methodology for the formation and implementation of economic growth strategies.

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)0.009
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.006
Scholarly communication0.0080.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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 designObservational
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

Citations31
Published2022
Admission routes1
Has abstractyes

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