Historical Development of Education and Learning in the Transmission of Miao Nationality Music in Yunnan Province, China
Bibliographic record
Abstract
Miao Nationality music represents a profound and longstanding musical heritage from the cultural legacy of the Miao community in Yunnan Province, China. This qualitative research study endeavor aims to explore the historical development and transmission of Miao Nationality music, emphasizing its significance within the context of ethnomusicology, cultural transmission theory, and education theory. The chosen research site strategically focuses on Yunnan Province, China, especially targeting regions with substantial Miao populace and deeply entranced musical traditions. Seven key informants, including elder musicians, music educators, community leaders, and cultural enthusiasts, were purposefully sampled to provide expert insights. A thematic analysis of qualitative data gathered through interviews and observations revealed important information about the ancient roots of Miao Nationality music, how it is used in everyday life, how cultural exchanges affect it, and how important it is for traditional instruments, vocal styles, and passing down the music from one generation to the next. The study suggests strategies for safeguarding and disseminating Miao Nationality music, emphasizing cultural awareness, technological advancements, support for inheritors, environmental protection, and innovative communication methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".