Transmission and Preservation of music of the Laos Vieng Ethnics Group at Tumbol Don kha, U-thong Distric SuphanBuri province
Bibliographic record
Abstract
Music Vieng, Laos, Vientiane, Lao ethnic group identity. And this is something that \nrepresents the cultural and traditional beliefs from the past, which is very important and plays \na role in Vientiane, Lao ethnic group. Today This research aims to 1. To study the music of \nethnic Lao Wiang Thong, Suphan Buri Province Don Kha 2. To study the musical heritage \nand preservation of ethnic groups in Laos Vieng Thong, Suphan Buri Province Don Kha. \nThe results showed that the music of ethnic Lao Wiang Thong, Suphan Buri Province \nDon Kha. Originally a folk music as well as the East. Originally, Canada is the main instrument. \nVientiane, Laos ethnic group influenced society. And culture outside to inside. The \ngroup of friends and was popular with the younger generation. Lack of support from local \norganizations People who know less. Make music, ethnic Vieng, Laos began to fade with time. \nceremony, a ritual is believed the disease. Similar to the medium by a worship of ancestors. \nConservation and musical heritage of ethnic groups in Laos Don Kha Vieng Thong, \nSuphan Buri. Are passed down from generation to generation through teaching, saying to \neach other in kinship. There are performances at various events Playing music in various \napplications And shows for visitors to watch.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".