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Record W4391064760 · doi:10.5539/jel.v13n2p75

Education and Learning Guidelines for the Preservation and Protection of Qinghai Mongolian Folk Songs in China

2024· article· en· W4391064760 on OpenAlexvenueno aff
Genqiqige Meng, Sayam Chuangprakhon

Bibliographic record

VenueJournal of Education and Learning · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
FundersMahasarakham University
KeywordsSafeguardingIntangible cultural heritageContext (archaeology)ChinaCultural heritageGovernment (linguistics)Political scienceSociologyPedagogyEngineering ethicsGeographyEngineeringLawArchaeologyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.141
GPT teacher head0.329
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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