Genre-format features of modern entertainment television
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The highly competitive digital environment makes it necessary to study the content strategies of TV channels that continue to attract a mass audience to the screens. In the example of the program grid of the STS, TNT, and Pyatnitsa TV channels, the genre and format features of entertainment TV were analyzed. The formats in demand for programming are identified, and the seasonal (from August to December 2021) and weekly dynamics of content strategies (Tuesday, Friday, and Sunday) are shown. The list of entertainment television formats was compiled, and the research matrix was created based on the study of theoretical sources and a pilot study. The analysis showed that the selected TV channels have different content strategies. However, more successful TV channels that have been on the market for longer are building their content strategy, turning mainly to non-journalistic content formats, primarily television films and series. Among journalistic entertainment programs, humorous shows, travel shows, and reality shows were in demand. The content strategies of all TV channels on weekdays, Fridays, and Sundays were different.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it