Bibliographic record
Abstract
Staring Wei Jie to Death uses the notion of evocation to give musical form to a peculiar story from ancient China. Wei Jie is a historical figure who served as a court official under the Jin dynasty during the late 3rd to early 4th centuries C.E. The Book of Jin relates that he was legendary even in his own time for his astonishing physical beauty, and that it proved to be the cause of his death. For when the Jin empire was threatened by barbarian invaders, Wei Jie fled south, to the city known today as Nanjing; there, people were so eager to catch a glimpse of his unearthly beauty that a crowd gathered to see him arrive. But Wei Jie, frail in health, could not withstand the force of their collective gaze, and thus, the story goes, he was stared to death. Rather than narrating events in a linear or programmatic fashion, Staring Wei Jie to Death instead takes certain aural “cues” from the ancient text and calls upon the orchestra to evoke the textual narrative by giving sound to key elements associated with it. Each of the work’s four sections is constructed around one of these elements, moving from sonically “concrete” to “abstract”: the ringing of jade in “The Man of Jade” (jade being a Chinese metaphor for beauty), the din of battle in “Great Chaos under Heaven,” the remote splendor of “Ancient Nanjing,” and finally, the consuming power of the gaze in “Staring Wei Jie to Death.”
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".