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Record W4414392785 · doi:10.5430/wje.v15n3p24

Sustainable Transmission of Suzhou Pingtan Through Education and Community Engagement

2025· article· en· W4414392785 on OpenAlexvenueno aff
J Wang, Yotsapan Pantasri, Khomkrich Karin

Bibliographic record

VenueWorld Journal of Education · 2025
Typearticle
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsGrassrootsThematic analysisCommunity engagementParticipant observationQualitative researchLeverage (statistics)FieldnotesCommunity educationNarrativePublic engagement

Abstract

fetched live from OpenAlex

Suzhou Pingtan, a traditional narrative musical art of the Jiangnan region, is facing challenges in transmission due to urbanization, modernization, and the decline in the Suzhou dialect. This study investigates how community engagement contributes to the sustainable transmission of Pingtan by focusing on educational outreach, intergenerational learning, and community-led promotion. Conducted in Gusu District, Suzhou, the research employed qualitative methods including participant observation, semi-structured interviews with three key informants, and document analysis. Thematic analysis revealed that informal educational initiatives in community spaces spark initial cultural interest, master-apprentice mentorship preserves authenticity and dialectical expression, and public cultural events expand the art form’s reach while fostering cultural pride. Interconnected pathways form a self-reinforcing cycle, maintaining both artistic integrity and social relevance. The findings emphasize the value of integrating grassroots participation into heritage preservation strategies, suggesting that future initiatives should enhance school–community partnerships, promote cross-generational mentorship, and leverage digital media for broader outreach. By emphasizing community agency, the study offers practical insights for policymakers, educators, and cultural practitioners in sustaining Suzhou Pingtan as a living tradition.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.296
Teacher spread0.278 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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