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Record W4328004521 · doi:10.55365/1923.x2022.20.113

Assessment of the State of the Entertainment Services Market Under Covid-19 Restrictive Measures

2022· article· en· W4328004521 on OpenAlexvenueno aff
Bokhodir Isroilov, Bozor Tukhliev, Boburshoh Ibragimov, Mokhigul Kutbitdinova, Almaz Sandy

Bibliographic record

VenueReview of Economics and Finance · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsEntertainmentBusinessEntertainment industrySupply and demandMarketingEconomicsMicroeconomicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.225
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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