Bibliographic record
Abstract
Devolving Songs is a circa twenty-seven-minute piece for fifteen-instrument ensemble, two vocal soloists, and three instrumental soloists.The piece consists of eight settings of Wallace Stevens's "From the Misery of Don Joost," a poem from the point of view of a Don Quixote-like character about the difficulty of change, the dissonance between bodily and intellectual knowledge, and the haunting echoes of a violent life.Song I, the "primary" setting, uses the text in its entirety.In Songs II-V, the text morphs, fragments, and devolves, while the musical material dissolves into a timbral shadow of the original.In Songs VI-VIII, a new language, both musical and textual, arises from the debris left over by this process of devolution.Devolving Songs represents a pivotal nexus in my aesthetic inquiry into semiotics, musical meaning, Mannerism, and the use of text and music in combination.Semiotics, the study of signs, along with other models of musical meaning from disciplines such as cognitive metaphor theory, inform the way the piece is structured, the dissonant relationships between materials in the primary setting, and the framing of the transformation from Song I to V as traversal through members of paradigms on various levels of scale.Mannerism informs the transformation of pitch systems used in Devolving Songs, the disjunctive aesthetic of the early songs, and forms the aesthetic basis of my denaturing and reuse of old styles and references.Finally, Devolving Songs takes lessons learned from earlier experiments in language-based music and applies them to a more traditional relationship between music and text.What results is a project of semantic destruction and reconstruction, as a broken mode of linguistic meaning is reconstituted into a new utterance of musical meaning.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.023 |
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".