COMMUNICATIVE FEATURES OF UKRAINIAN VIDEO BLOGS ON THE EXAMPLE OF YOUTUBE-CHANNELS OF «TORONTO TV», YANINA SOKOLOVA, AND OSTAP DROZDOV
Bibliographic record
Abstract
The article is devoted to the study of the Ukrainian segment of video blogging as one of the most popular types of the functioning of the modern blogosphere. The content and statistics of popular video blogs were studied on the example of YouTube channels of Ukrainian bloggers and famous journalists. Today we are witnessing the rapid development of technologies that help journalists become better, and the creators of media content to work more quickly and ensure the completeness of the information. With the help of Internet communication, new ways of disseminating information have appeared in journalism. Journalists more often create their blogs on various platforms. Blogosphere video content has become very popular among the Ukrainian audience on YouTube because today the video format is the most effective in terms of communication. The YouTube social network partially replaces television, and the variety of thematic content is ably adapted to a wide audience. The paper analyzes Ukrainian blogs managed by journalists, where they publish different content formats. Therefore, the presentation of various examples of video blogs in our work helps to understand the specifics of Ukrainian blogging at its current stage of development. After all, videos of popular people such as Michael Shchur, Yanina Sokolova, Ostap Drozdov demonstrate the peculiarities of Ukrainian popular video content. For the research, we chose those blogs that are currently relevant to Ukrainian YouTube and have their specifics and uniqueness. The main objective of a blogger is to react quickly to the flow of information because the rating of the channel being monetized depends on it. With the help of statistical data, we can conclude that the Ukrainian audience is interested in a wide range of different information. Viewers now value the independent opinion of bloggers and more often listen to it. Every important event is covered by bloggers promptly. And the format in which it is presented depends on the individual style of the author and the concept of his channel. We can conclude that the video content of the modern blogosphere is developing rapidly. This provides the audience with information for different tastes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".