Bibliographic record
Abstract
Even taking into account the difficult conditions of life in Ukraine, society always strives to restore itself by filling itself with positive emotions. Considering these aspects, the theater industry is still growing even faster than during the pandemic since 2020. Accordingly, today marketing is an important tool for the successful functioning and development of theatrical art, ensuring its accessibility, popularity and financial stability. The purpose of the article is to analyze the effectiveness of marketing management support in the field of theatrical art. The results of the analysis of queries in the Google search network, scientometric databases Scopus and Web of Science confirm the relevance of the researched topic. An analysis of the dynamics of search queries for the period from 2004 to May 2024 showed significant fluctuations in interest in marketing management and theater arts, which were caused by economic crises and the COVID-19 pandemic. After the recovery in 2022, new marketing platforms such as TikTok have contributed to positive changes in the field of marketing management. Geographical analysis shows a predominance of interest in theater arts in Canada, Australia, Great Britain and Ireland, while in China, Brazil, Germany and Ukraine there is a greater interest in marketing management. The author revealed an increase in the level of interest among the scientific community in the field of research into the effectiveness of marketing management in the theater industry. This is evidenced by the general increase in the number of scientific works over the past five years by almost 20%, taking into account the rather wide geography of publishing activity. A competitive analysis of four of the largest and most popular theaters of Ukraine - Lesya Ukrainka National Academic Drama Theater, Ivan Franko National Academic Drama Theater, Solomiya Krushelnytska Lviv National Academic Opera and Ballet Theater and Kyiv National Academic Molodyy Theatre - emphasized the importance of modern marketing strategies and active using social media to successfully promote theater productions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".