İnformasiya təhlükəsizliyi, hüquqi əsasları, müqayisəli yanaşma
Bibliographic record
Abstract
It should be emphasized that in the experience of the countries across the world there were the first laws on the information protection and national security information too. In France, Italy, Spain, Portugal, Denmark, the Netherlands and other countries, laws were passed which allowed everyone to get acquainted with information on the activities of government agencies and in the United States, Canada, Australia and New Zealand, in accordance with these laws, citizens have the opportunity to obtain information directly about management. Particular emphasis should be put on that, in order to take the necessary measures to ensure information security, the issue of the formation of international legal mechanisms and the national legal and regulatory framework is of special importance and should be considered not in the context of individual countries but in international global information security. The Republic of Azerbaijan in the field of information security carries out measures for international cooperation with the CIS countries, as substantive legal frameworks. Current trends in development create the opportunity to assert that the place and role of information security in the system of ensuring national security will be strengthened, which is specifically argued in the law books.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".