ANALYSIS OF WORLD EXPERIENCE IN THE FIELD OF IMPROVING THE PROCESS OF INTERACTION OF CIVIL EMPLOYEES IN CYBERSPACE
Bibliographic record
Abstract
In this article, the author analyzes world experience in the field of improving the process of interaction of civil servants in cyberspace, which consists in analyzing social engineering, which is presented as an effective mechanism for influencing the formation, regulation and development of digital behavior of public servants. The essence of which is the conscious use of psychological, communicative and social tools aimed at adapting personnel to the digital environment, establishing the principles of digital ethics and reducing the level of cyber threats. It is noted that social engineering can be considered, in particular, as a tool for transforming personnel policy, and involve the use of socio-psychological mechanisms for modeling the behavior of civil servants. The author emphasizes that the digital behavior of civil servants is largely shaped by established or informal standards that operate within the professional community. The experience of various countries (Singapore, Denmark, Canada, Estonia, Bulgaria, the Netherlands, the United Kingdom, etc.) on the issue of regulating the digital behavior of civil servants is analyzed, which showed that the comparative analysis of national approaches to regulating the digital behavior of civil servants made it possible to identify both common principles and specific features of different countries. Key among them are ethical governance, transparency, accountability, respect for human rights and the development of digital competence in the public sector. The conclusion is made that international practice demonstrates the multi-vector nature of approaches to the formation of ethical behavior of civil servants in the digital space. Based on the analyzed international experience, it is advisable to introduce a phased model of digital ethics in the civil service in Ukraine, focusing on the successful practices of the above-mentioned countries.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".