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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 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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.130

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.018
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 teacher head, 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

Citations5
Published2023
Admission routes1
Has abstractyes

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