An Intelligent Multi-Stage Model for Countering the Impact of Disinformation on the Cybersecurity System
Bibliographic record
Abstract
The main purpose of the paper is to form an intelligent multi-stage model for counteracting the negative impact of disinformation on the cybersecurity system.The research methodology involves the use of various modeling methods, including the construction of diagrams, intelligent models and matrices.The main results of the study are modeling the process of counteracting the negative impact of disinformation in the cybersecurity system for a particular region.Successful should be considered the application of a methodical approach to solving the tasks.All stages of the modeling technique were successfully completed.As a result of the study, the main intellectual multi-stage model for counteracting the negative impact of disinformation on the cybersecurity system was identified and presented.The study has a number of limitations related to the fact that it does not allow to cover all types of disinformation.Only those that, according to the authors, were of the greatest relevance today, were selected for modeling.Further research should be devoted to expanding the intellectual model, taking into account new factors of the negative impact of disinformation on the cybersecurity system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".