STRATEGY FEATURES OF INFORMATISATION SOCIETY IN THE COUNTRIES OF THE WORLD
Bibliographic record
Abstract
The relevance of research caused by global informatisation and active formation of open information society.Special attention is paid to the unequal distribution of information-communication technologies (ICT) between developed countries and in the society.The purpose of the paper is to analyze different strategies and electronic programs for ICT implementation in different countries.The paper shows that every country has its own way of development of new technologies and transition to the new information epoch.Methodology.Article highlights formation policy of information society for selected countries: USA, Canada, European Union, Japan and Ukraine.The paper analysis strategies and electronic programs for ICT implementation.The comparative analysis and major trends of information society in the world were identified.The historical chronology of adoption of legal acts for development of information society was recreated.Different programs that implement ICT in all spheres of human activity were examined.The legislation is the foundation of all ICT programs that are implemented in the country.Less developed countries should analyze and take into the consideration the experience of countries that have already e-Government.Results.According to the experience of examined countries we can see that the degree of informatisation of the country depends on the direction of the government policy.The formation of the legislative foundation for each country was made approximately in one time but the pace of ICT implementation is quite different.The formation on legislation foundation began nearly in one time in each country but the pace of ICT implementation are quite different.All countries follow global trends and basic principles of information society formation and in the same time include national aspects into the ICT programs.The main aim of ICT program is the distribution of information and communication infrastructure in all areas of human activity, implementation of e-commerce, e-government, etc.In table 1 in the paper there is summarize statistics where is shown several aspects of informatisation: the first year of ICT regulation, the usage of e-government, expert evaluation of dissemination of ICT programs, the percentage of Internet users, expenditure on ICT (percent of GDP).According to the data we can see how government policy can influence on development of ICT in the country.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".