The Impact of Cybersecurity, IT Spending, and Innovation on Economic Growth in 2023
Bibliographic record
Abstract
This study seeks to examine the influence of cyber security, IT expenditure, and innovation on economic development across a sample of 30 nations, utilizing cross-sectional data from 2023.This is happening because the digital transition is speeding up and digital variables are playing a bigger role in supporting global economic growth.The study employed a quantitative analytical framework, utilizing the Ordinary Least Squares (OLS) method with EViews12 to assess the correlation between the independent variables (cyber security, IT expenditure, and innovation) and the dependent variable (economic growth).The results indicated that cyber security exerts a positive and considerable influence on economic growth, underscoring the necessity of establishing a safe digital environment to foster trust and stability.The effect of IT expenditure was favorable but not very big, which shows that spending efficiency varies from country to country.Innovation has a negative and substantial effect, which may be due to a difference between the results of innovation and how it is actually used in some nations .The study suggested that to maintain long-term growth, we should improve the efficiency of technology expenditure, build cyber security infrastructure, and try to close the gap between scientific research and the job market.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".