Negotiating Tradition and Innovation: Digital Heritage Practices and Cultural Sustainability of Suzhou Pingtan in Contemporary China
Bibliographic record
Abstract
As a traditional Chinese narrative-singing art, Suzhou Pingtan is encountering growing challenges of preservation and renewal due to population aging and technological shifts. The Pingtan community’s heritage inheritance and cultural identity are being impacted by the digital transformation, underscoring the conflict between cultural preservation and adaptable solutions. Using qualitative field observations, interviews with heritage practitioners and stakeholders, and document analysis, this study argues that online platforms have expanded access to heritage, involving the younger generation and offering economic opportunities. Traditional performances and digital media with an entertainment focus are deeply at odds because of concerns about digital literacy and language authenticity. The “Traditional Music Ecosystem Model in Digital Transformation,” which is put forth in this article, sees cultural sustainability as dynamic adaptation as opposed to static preservation. For sustainable heritage development, digital platforms, practitioner communities, and cultural institutions must have a strategic relationship. Through the application of research findings to globally endangered cultural practices, this study contributes to the theoretical understanding of heritage sustainability in the digital media environment.
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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 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 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".