Marketing Communications Management in the Public Administration System
Bibliographic record
Abstract
The issues of building effective marketing communications systems at the level of public administration are discussed in the article. Most marketing communications research focuses on the private sector. Marketing communications in the public sector are comprehensive and multi-functional. So, marketing communications in public administration are considered not only as a complex of information and communication mechanisms. They also include political, social, economic and cultural aspects. The marketing communications system should be formed both on the domestic market (provision of administrative services to the population and formation of a positive image of state power) and external (establishment of cooperation with international partners and conduct of international policy). According to statistics, the system of information support of public administration is considered relevant by more than 60% of the population of Ukraine. Therefore, given the relevance of this issue, it is useful to determine its practical significance. The establishment of a marketing communications system in public administration should provide the population with mobile provision of administrative services of high quality, the creation of systems of democratic civil society through the development of electronic petitions, the formation of a favorable image of the state to ensure high support for the processes of state creation among the population and at the international level to attract additional investments and ensure tourist attractiveness. Among the current directions of development of marketing communications of public administration are advertising, PR-technologies, image and brand construction, fight against “negative” and fake information, provision of electronic management and provision of administrative services online. However, providing the population with access to the Internet also remains an important issue, as it is this resource that is the main one for the introduction of digitization and digitalization processes in the country.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".