Bibliographic record
Abstract
In the information and knowledge society, information and knowledge have become important media to improve national competitiveness. Improving the information and communication technology has brought out the various information invasions as well as information usages through broad information and knowledge. So a plan for national informatization should be involved in the ways to overcome local and national digital divides as well as information problems. The national informatization should develop through balanced international cooperation for IT utility based on information security. This study is for evaluating information security policies with information society adaptability model(ISAM) in 47 countries. An indicator of information security policy has three consistent categories: legal and institutional, physical and technical, and administrative information securities. Each indicator has increased by the values of experts in 47 countries. An indicator of national IT utility has three consistent categories: IT utilities by government, business, and the public. So two comparison factors have main-and sub-indicators. After it analyzed information society adaptability, the USA, Singapore, Canada were evaluated as high adaptable countries for informatization of 47 countries. Indonesia, Venezuela, Russia were evaluated as worst countries of them. As a case of South Korea was evaluated at 18th rank in 47 countries, so South Korea should rebuild a national policy for information security with participation of government, business, and the public for national balanced development of informatization.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.014 |
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".