Current Status and Prospect of Russian Music Industry: Focusing on Music Streaming Market
Bibliographic record
Abstract
Within the world music market, streaming services are booming and are becoming a trend. As in the global trend, the most rapid growth in the music market in Russia is in the "streaming" category. Analyst firm ‘GlobalWebIndex’ reported in the fourth quarter of 2016 that 62% of the world"s people listen to music through streaming services such as ‘Sporty’ or ‘Apple music’. And about 13% of them are using paid subscription services. In Russia, people who use streaming services for free are increasingly turning to paying subscribers. In 2017, pay-per-view subscribers will increase by 2.6 times in a year and clean up concerns that if a paid subscription is implemented in earnest, most people will not use the service. In 2018, more than 2 million users in Russia have signed up to pay music content services, and this growth is expected to continue steadily, reaching between 10 million and 12 million as early as 2020. In this article, we will examine the status of Russian music industry since 2010. In particular, we will focus on the changes between 2015 and 2017, when streaming services began to develop in earnest. We will also compare the service quality of domestic and international music streaming companies operating in Russia and analyze how Russians listen to music and their musical preference trends. This will provide the contents and outline of the attention of Korean and foreign companies considering the advancement into the Russian music market in the future. Furthermore, based on the results of this study, we will try to find the first task and its solution for the development of the Russian music streaming market, and we will measure the prospect of the Russian music industry in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".