Economic Security Management for Sustainable Planning
Bibliographic record
Abstract
Sustainable development is one of the most important challenges facing mankind today.The essence of sustainable development is to maximize economic and social benefits while protecting the environment and ensuring the long-term sustainable use of natural resources.In economic terms, sustainable development means not only the growth of the national economy and per capita income, but also improvements in all aspects of social security.The main purpose of this study is to model the assessment of the negative impact of threats on the information space of sustainable development planning within the context of managing economic security in a given socio-economic system.The research methodology involves the use of modern econometric methods based on fuzzy relations theory.This methodology fully contributed to achieving the set goals and resulted in justifying the application of weighted measures for each class of security threats in the information support system for sustainable development planning.Additionally, it was shown to be expedient to develop a clear plan for organizing information space protection while ensuring a balance between the level of information risk and the acceptable costs of ensuring economic security during planning for sustainable development in a given socio-economic system.This study has limitations, and they are related to the narrowness of identified threats and the choice of only one specific socio-economic system as an example.These aspects should be broadened in future research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".