Prerequisites for Process Management Implementation in the Public Administration of Ukraine
Bibliographic record
Abstract
The main objective of the study is to analyse the prerequisites for the implementation of process management in public administration of Ukraine. In the process of identifying the presence of certain prerequisites in modern Ukrainian realities, methods of analysis of economic statistics and international indices that determine the level of technological and economic development of the country, as well as readiness to use information and communication technologies have been used. It has been determined that the evolution of management systems is derived from the development of managed objects. Therefore, the development of technologies as a way to transform matter, energy and information is the main determinant of the quality characteristics of management systems. It has been proved that given the global openness of the national economy and in a situation of insufficient technological level, there are opportunities for catch-up development in Ukraine, based on the introduction of digitalization technologies, which are used mainly in the sphere of services – financial, educational, public, etc. As a result, the introduction of information technologies will facilitate the transition to new rules for companies, organizations and government agencies. Taking this into account, there is an urgent opportunity in Ukraine to introduce process management tools and improve the efficiency of public administration and local self-government systems. However, changing the management system, in addition to process modelling and optimization, will require the transformation of cognitive models of civil servants as a sufficient condition for the effectiveness of the implemented system.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".