Transmission of Shangqiu Siping Diao through Chinese Opera Education for Cultural Sustainability
Bibliographic record
Abstract
In the face of rapid modernization and declining public engagement, Shangqiu Siping Diao—an endangered regional opera tradition in Henan Province, China—has experienced critical challenges to its continuity. This study aims to investigate the transmission of Shangqiu Siping Diao through Chinese opera education in order to promote cultural sustainability. Conducted in Liangyuan District, Shangqiu City, where the Siping Diao Theatre Troupe and Art Research Centre are located, the research employed a qualitative methodology grounded in ethnomusicology and arts education. Seven informants were purposively selected, including three key informants who are nationally recognized opera masters and four general informants who are young students or performers engaged in formal training. Data were collected through semi-structured interviews, field observations, and document analysis, then analyzed thematically to identify educational strategies and their cultural impact. Results reveal a three-phase developmental trajectory: promotion (2006–2011), innovation (2012–2021), and stabilization (2022–2025), demonstrating how educational integration has revitalized this art form. Initiatives such as school-based opera creation, incorporation of representative repertoires like Lv Meng Zheng Gan Zhai, and community outreach performances have enabled generational transmission and fostered cultural identity among youth. The study suggests that combining traditional pedagogy with digital media and public engagement strengthens cultural sustainability. This model may be applied to other regional operas facing similar threats.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".