The Media Use of Liuzhou Folk Song Inheritance in China: According to the Investigation in Yufeng Folk Song Field Under Yufeng Mountain in Liuzhou City
Bibliographic record
Abstract
According to the existing research, with modern urban civilization continuously extends into traditional rural society, the survival environment of traditional national culture is suffered from the impact of various aspects, including development of new media technology. However, in the process of the technical urbanization, Liuzhou folk songs have no depression from the impact. On the contrary, because of new methods to be imported into its inheritance, Liuzhou folk songs are presenting a boom. The fieldwork found that Liuzhou folk song inheritance is experiencing shift which is from the traditional mentoring to medium teaching in the process of the modernization. These media include textbooks about folk song creating, improvised dish homemade by singers, the network video resources, and network spontaneous learning community through QQ group and other social software. The active medium use to make folk heritage can adapt to the environment change, and make folk heritage for innovation and development. That has a certain advantage. But in other hand, there has been some limitation that because of the excessive emphasis on written rules and figures of speech, it should have a certain damage on the bearing cultural connotation of Liuzhou folk songs. People should pay more attention to it.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".