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Record W4388829189 · doi:10.54097/ijeh.v11i1.12740

The Potential of the Digital Economy: A Comparative Assessment of Key Countries' Cybersecurity

2023· article· en· W4388829189 on OpenAlexaboutno aff
Xiuli Chen, Tao Wang, Xiaoxi Lin, Dylan Elliott Hinde, Qianhao Yan, Zmire Zeljana

Bibliographic record

VenueInternational Journal of Education and Humanities · 2023
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsnot available
Fundersnot available
KeywordsDigital economySWOT analysisChinaPromotion (chess)BusinessKey (lock)Digital transformationComputer securityPolitical scienceMarketingComputer science

Abstract

fetched live from OpenAlex

This study investigates the digital economy capacities and cyber challenges in key nations, including China, India, Japan, Australia, South Korea, Canada, Singapore, and the United States. Using a SWOT analysis and comparative approach with data from the National Cyber Security Index (NCSI), the research focuses on digital infrastructure, cybersecurity, innovation promotion, digital divide, and regulatory challenges. The findings underline the importance of a comprehensive approach to cybersecurity, addressing national and international concerns. The study also highlights the significance of strong digital infrastructure, innovation ecosystem, and robust cybersecurity framework for success in the digital era. Although some countries have emerged as leaders in the digital economy, others like China and India are making progress in building their digital capacities. The analysis emphasizes the need for continued investment in digital infrastructure, fostering innovation, and enhancing cybersecurity to maintain competitiveness in the global digital landscape.

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)0.002
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.012
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0000.003
Research integrity0.0000.000
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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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