Transmission of Yugu Folk Song Knowledge in Sunan County, Gansu Province, China
Bibliographic record
Abstract
The qualitative research method is used in this study to investigate ways to transmit Yugu folk song knowledge in Sunan County, Gansu Province, China. The area was selected in Sunan County, Gansu Province, China. The fieldwork method is mainly used for collecting data, together with the information from the document. The study results are as follows: The origins of the Yugu people are not single but multiple. They are a new ethnic community formed by the integration of the ancient Uighurs and the ancient Mongolian tribes. The Yugu people are currently the best-preserved ethnic group in the world’s Ancient Turkic language, due to the characteristics of the language and the geomorphological characteristics of the Yugu area, making the Yugu music unique and different from the music of the surrounding Han and other ethnic groups. The development of Yugu folk songs can be divided into five periods: 1) the Mobei period before 840 AD and the production period of Yugu ancestral folk songs; 2) from 840 AD to the beginning of the 16th century, the gradual development of the art of Hui folk songs; 3) from the beginning of the 16th century to 1953, the excavation period of traditional folk songs of the Yugu people; 4) from 1953 to 1990, the prolific period of Yugu folk song creation; 5) since 1990, the trough period of traditional Yugu folk songs and the development period of newly created songs.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".