INNOVATIVE MANAGEMENT OF CULTURAL, EDUCATIONAL AND SCIENTIFIC PROJECTS IN THE PUBLIC SECTOR: ECONOMIC TOOLS AND MARKETING STRATEGIES
Bibliographic record
Abstract
In the context of rapid digitalization and global challenges of today, cultural and scientific projects are becoming key instruments of social development, national identity formation, and cultural, educational, and scientific diplomacy. The study examines innovative management as an effective management model that enhances the effectiveness of promoting such projects in the public sector. The aim of the work is to identify effective economic tools and digital marketing strategies that can be integrated into the process of managing cultural and educational-scientific initiatives. The object of analysis was government programs, institutional models, and communication practices in the public sector in five countries: Sweden, Germany, South Korea, Brazil, and Canada. The study used methods of content analysis of official documents and digital platforms, case studies of successful initiatives, as well as elements of comparative analysis and interpretation of public management and administration practices. The theoretical basis was formed by the concepts of open innovation, disruptive thinking, cultural economy, and project management. The main mechanisms of economic support (from budget financing to social investments) used in more than 30 national programs were studied. An analysis of nearly 100 cases of digital communication showed that the most popular communication channels are Instagram, Facebook, YouTube, and TikTok, which together attract more than 60% of the target audience. Projects in the fields of culture, education, and science were examined, such as KunstDigital, AI & Culture Lab, and ScienceStories EU, confirming the effectiveness of combining VR/AR, NFT, artificial intelligence, and influencer marketing in promoting non-commercial projects. The novelty of the research lies in the combination of quantitative analysis of digital channels, qualitative case studies, and evaluation of financial instruments, which until now have been considered fragmentarily in the scientific literature. The practical value of the results lies in the possibility of applying the developed model in the formation of state strategies for digital transformation in the fields of education, culture, and science, the development of innovation support programs, and the evaluation of the effectiveness of mixed marketing strategies in working with target audiences in the field of public administration.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".