Using an integrated MCDM technique to assess the development of information and communication technologies in G7 countries
Bibliographic record
Abstract
There is information and communication technology greatly helped various aspects of modern civilizations (ICT).Due to its rapid development and pervasive use across many industries, ICT in today's social and economic development makes a significant impact.Governments are continuously attempting to provide improved regulations and proposals to strengthen their ICT infrastructure, taking into account both the good and negative consequences.The fifth most important requirement for humans is knowledge.For sustainable development in this article as a measure of the importance of ICTs is estimated.It was investigated whether ICTs were important for sustainable development.It has also been assessed how other ICT components affect knowledge management.But in order to propose better policies, one must have a comprehensive understanding of both past and contemporary practices.In this sense, it is crucial to assess how well each nation is doing in terms of the advancement of information technology.This study uses an integrated MCDM approach to evaluate the development of ICT using social and economic factors.The G7 nations are assessed using actual data from OECD statistics as major industrialized countries to establish a realistic standard.This comparison of G7 country performance takes into account six significant variables, including Internet access, ICT employment, ICT goods export, ICT investment and ICT value addition.Results: The findings show that the United States and Japan are leading nations in ICT development, whereas Italy and Canada do poorly and need to strengthen their IT policies in order to perform better.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".