Algorithmic Framework for an Information System Ensuring Sustainable Development and National Security
Bibliographic record
Abstract
This study primarily aims to develop an algorithmic framework for constructing an information system dedicated to ensuring sustainable development and national security. The unique functionalities of this framework are manifested through the deployment of functional blocks, which, when represented with appropriate vectors and arrows, can facilitate superior organization and visualization of information. The scope of investigation is confined to the national information security system of a specific country. The primary scientific task undertaken in this study involves the modeling of an algorithm for constructing an information system that can ensure sustainable development and security. To accomplish this, the cutting-edge graphical modeling language of the Data Flow Diagram (DFD) standard is employed. The outcome of this study is a model that outlines the construction of an algorithm for the formation of an information system geared towards sustainable development and security. The novelty of this study lies in the methodological approach taken to develop the algorithm, which introduces a fresh perspective to addressing the issues at hand. The innovative aspect of this study is revealed in the modeled process of forming an information security system. However, the study is limited by the specific characteristics of the national security system of a single country. Future research in this domain should focus on modeling the integration of digital technologies into the national security system.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".