Bibliographic record
Abstract
Due to the development of a fast-paced society, high pressure originating from work and study has shown great influence on people’s daily life. In order to alleviate the high pressure, people start to find more entertainment. This trend led to changes in news content and its form. Under the development of a socialist market economy, media has to compete with each other, satisfying the entertainment requirement of the public, at the same time spreading news. Finally, they can win their market share under hyper-commercialism. From the lens of spread, the phenomenon of pan-entertainment news will be researched. In this paper, Weibo, one of the largest social media platforms, serves as the main research object. An analysis of trending topics on Weibo will be carried out to see how pan-entertainment news spreads and its influence. In addition to that, the reason why pan-entertainment appeared will be discussed. After the analysis of Weibo trending topics, it can be noticed that the public prefer to trust celebrities’ words rather than authority words. From introspection of media and target audience, a correct guide can be achieved, which can make a contribution to the positive development of entertainment news.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".