Bibliographic record
Abstract
It’s important for content of Pay TV Channels to be specialized, individualized and branded which becomes the purpose of specialization and individualization. Only with strong brand effect, can Pay TV Channels succeed in competing with Free TV channels and other media. Differentiation founded on TV brands will be beneficial not only for enhancing brand awareness and customer satisfaction, but for establishing TV media’s strategy system. For instance, in 2004 to 2008, China DTV Media, China’s leading Pay TV operator, put up a brand evaluation system containing market share, brand awareness, brand loyalty as measuring standards, with which the brand-building has been token up during market operation. Actually, in the long-term development, many Free TV brands have been created in China. Among winners of the Top Ten Programs awarded by China Broadcasting and Television Association in 2005, there are five TV programs, all of which are free programs, including Law Online (in CCTV), Nanjing Alive (in Nanjing Broadcast Television), Night News (in Heilongjiang TV), True Love Story (in Hebei TV) and The Past (in Hubei TV) (Zhang Junchan & LuiPeng, 2006). While compared with Brand-Building on Free TV Channels, in China, Pay TV Channels hasn’t walk the path of Brand-Building really. How can it move on better? In this section, there will be some analysis about it.
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 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 | 0.002 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".