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Record W4225143732 · doi:10.18280/ijsse.120210

Modeling Ways of Counteraction to External Threats to Corporate Security of Engineering Enterprises in the Context of COVID-19

2022· article· en· W4225143732 on OpenAlexvenueno aff
Svitlana Kryshtanovych, Nadiya Lyubomudrova, Sergey Tymofeev, Olga Shmygel, Anna Komisarenko

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanking, Crisis Management, COVID-19 Impact
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Coronavirus disease 2019 (COVID-19)BusinessEconomic securityCorporate securityComputer securityRisk analysis (engineering)Computer scienceEconomicsEconomic growthCorporate governanceFinance

Abstract

fetched live from OpenAlex

The primary goal of the study is to form an effective model for countering external threats to the corporate security of engineering enterprises in the context of COVID-19. The priority of forming a methodological basis for ensuring the corporate security of enterprises today is determined primarily by two key circumstances. First, is the need to create safe conditions for development under the influence of COVID-19. Secondly, the importance of the role of corporations functioning today, which they already play in economic processes. The modeling methodology was applied to show the functioning of the system for counteracting external threats to the corporate security of engineering enterprises in the context of COVID-19. COVID-19 has had a significant impact on the activities of engineering enterprises around the world, especially concerning foreign economic activity. Because of the study, the main stages and processes of countering external threats to the corporate security of engineering enterprises in the context of COVID-19 were identified.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.038
GPT teacher head0.265
Teacher spread0.228 · 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 designSimulation or modeling
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

Citations11
Published2022
Admission routes1
Has abstractyes

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