Interactions of Folk Melody and Transformational (Dis)continuities in Chen Yi’s <i>Ba Ban</i> (1999)
Bibliographic record
Abstract
Chen Yi’s Ba Ban (1999) for solo piano, like many works of Western-trained Chinese composers, situates fragments of evocative traditional folk melody within a post-tonal discourse that is well described by transformation theory. The eponymous folk tune that it quotes is a standard of the sizhu (“silk-and-bamboo”) repertoire. In sizhu performance practice, the evenly pulsed rhythm of the 68-beat melody is augmented and each pitch is highly “flowered,” that is, decorated. Chen’s piece, often simulating the timbral quality of sizhu heterophony, reproduces some of the directed temporal qualities of this repertoire by quoting distinctive phrases and elaborating their pitches. Intermingled with this discourse, however, it presents multilinear threads of motivic transformation through virtuoso figurations typical of Western piano repertoire. The free rhythm evokes a different folk music tradition, mountain song, that Chen mentions as inspiration. At first, as the post-tonal structures are introduced, they disrupt the linear continuity of the Ba Ban folk tune and create an undirected associative network. Eventually, however, they gain control over temporality as firmly as Ba Ban did at first, and then Ba Ban itself is transformed into ametrical pulse. Considering the contrasting gendered connotations of mountain song and sizhu , I suggest how my narrative of these rhythmic processes might resonate with some ideas of feminist theory.
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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 | 0.000 | 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 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".