Education and Learning Guidelines for the Preservation and Protection of Qinghai Mongolian Folk Songs in China
Bibliographic record
Abstract
This study aims to develop comprehensive education and learning guidelines aimed at preserving and protecting Qinghai Mongolian folk songs in China within the broader context of intangible cultural heritage. With a strong theoretical foundation emphasizing the role of education, interdisciplinary approaches, and community engagement, this study employs a multidisciplinary methodology. This includes an extensive literature review, expert consultations, fieldwork, and case studies to develop comprehensive education and learning guidelines for the safeguarding of Qinghai Mongolian folk songs. The historical evolution of Mongolian folk songs, legal frameworks, collaborative efforts, government-led initiatives, the role of social forces, published resources, and higher education institutions all feature prominently in the research results. These findings align with the theoretical principles outlined in the literature review, emphasizing the importance of education, interdisciplinary approaches, and community engagement in ICH preservation. The education and learning guidelines generated by this research serve as a valuable framework for the sustainable protection of Qinghai Mongolian folk songs and offer insights applicable to the preservation of intangible cultural heritage globally.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".