Bibliographic record
Abstract
It is no secret that COVID 19 and its consequences have affected almost all aspects of our life. We have become focused on life at home, the approach to work has changed, and the definition of remote work has taken root in our vocabulary. Despite all the negative aspects, the pandemic has accelerated the development of key technological trends, such as distance learning, telemedicine, remote work, online shopping, contactless payments, 3D printing, which leveled out supply disruptions, robotics, a new generation of 5G mobile communications with its capabilities, and of course, the online entertainment industry. Our consumer preferences have changed during this year; however, the need for entertainment has only increased. Many believe that nowadays, the Internet and television era is a thing of the past; nevertheless, statistics do not confirm this. In the third quarter of this year alone, global TV sales have increased by 12.9% over the same period in 2019, which is 38% more than in the previous quarter. Television viewing has increased, and television program views have skyrocketed. Streaming content has become even more popular; streaming services allow one to watch absolutely everything - movies, TV series, news at any time and from any device. All these processes are connected with the fact that, during the quarantine, cinemas were closed, the attendance of which has already decreased in recent years. They opened with restrictions on viewers’ seating; the premieres were postponed for a year, even two - until the spring of 2021 and 2022. This year, the world of the media and entertainment industry has become: remote, virtual, streaming and personalized. The driver is the consumer, so market players pay great attention to innovation, focusing on personalization. At the forefront of new technologies is the Disney company, which presents its films both in theatrical screenings and on its online platform. Television is also not left behind; on November 3, VGTRK launched its "Smotrim" media platform. The audience’s consumption habits have already changed, interest in media is increasing and moving towards digitalization. The pandemic has accelerated the process. How this is happening and what awaits the industry is covered in this article.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.141 | 0.032 |
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".