Assessment of the State of the Entertainment Services Market Under Covid-19 Restrictive Measures
Bibliographic record
Abstract
The relevance of this study lies in the fact that the growth of the entertainment services market as an industry worldwide suggests that it will continue to develop, despite the restrictive measures introduced due to the COVID-19 pandemic.The market that provides entertainment services is considered as a complex of interrelated socio-economic, legal, and organisational relations between the consumer and producers of entertainment-related services in purchase and sale.The demand and supply are crucial and topical features of this market since they enable communication between the seller and the consumer of entertainment-related services.The need for such services is directly dependent on a number of factors, such as the availability of free time, the level of solvency of residents of the country, seasonality, consumer preferences, etc.The purpose of the study is to conduct a summary analysis of the state of the entertainment services market in the conditions of the imposed anti-covid measures and to propose recommendations that can improve the performance of the main activities of the entertainment services market.The following methods were used in the study: analysis, synthesis, comparison, and economic and statistical analysis.The results obtained allow assessing the state of the entertainment services market during the period of restrictive anticovid measures and identifying those areas of activity of the entertainment services market that are currently the most promising in terms of subsequent development.
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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 | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".