The Countermeasures of Improving the Willingness to Pay for the Pay Channel
Bibliographic record
Abstract
Along with common use of the digital technology, the development of digital television industry has become the trend of the world. In July 2003, the State Administration of Radio came up with the conversion concept of digital television and approved 33 cities as the first batch of cable digital TV pilot. Since then, the number of cable television subscribers in China has been growing at a high speed, according to the data of the State Administration of Radio and Television. Until the end of January of 2011, China’s digital cable TV users has exceeded 90,000,000, reaching 90,392,000. The degree of wired digitalization reached 48.26%. (The basic users of cable TV were 187,300,000. China digital cable TV users increase steadily, and the level of cable digitalization rises steadily. However, the development of the digital pay channel of China is not so satisfactory. We should pay attention to some problems, for example, the purchase intention of pay channel is not so high in China. Here we are going to summarize the reason that the purchase intention of pay channel is not so high. Besides, we are going to try to find out the countermeasures.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".