Factor-Based Quantitative Comparison Analysis of the Inheritance of Intangible Cultural Heritage: A Case Study of Kunqu Opera between Chinese Mainland and Taiwan
Bibliographic record
Abstract
In 1940s, the Kuomintang (KMT) retreated to Taiwan, along with a lot of amateur artists accomplished in singing and dancing of Kunqu Opera. Due to unlike and separate social environments, Kunqu Opera developed into two different ways in Taiwan and Chinese mainland since then. In contrast with Taiwan’s choice to maintain the tradition of Kunqu Opera, especially that of 1930s as much as possible, Chinese mainland turns to modernize this art to cater to social trends. This paper analyses two versions of the same scene “Broken Bridge” (断桥) from Taiwan and Chinese mainland in spoken language, melody, literary form of lyrics, dance, stage set and costumes to try to find the factors that are not changed, which can be understood as the core factors with inherited cultural values of the intangible cultural heritage. Based on these core factors, the effective protection is possible. This research shows that although Kunqu Opera in Chinese mainland is gradually changing, particularly turning realistic as opposed to the one keeping impressionistic in Taiwan, there are some factors almost untransformed: the melody (kunqiang), literary form of lyrics (qupai style), costumes evolving from the dress of Ming dynasty. An effective protection method of Kunqu Opera should put emphasis on these factors.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".