Bibliographic record
Abstract
Sinfonia, 2018 is a three-movement work for wind ensemble, with a combined performance duration of approximately 25 minutes. The instrumentation consists of piccolo, flute 1+2, oboe 1, oboe 2 doubling English horn, clarinet in E flat, clarinet in B flat 1-3, bass clarinet in B flat, bassoon 1+2, soprano saxophone, alto saxophone 1+2, tenor saxophone, baritone saxophone, trumpet in B flat 1-3, horn in F 1-4, tenor trombone 1+2, bass trombone, euphonium, tuba and percussion (minimum 5 players). The compositional style employs a freely chromatic harmonic and melodic language with elements of extended tonality and with rhythms sometimes mimicking those of natural speech. Although open to multiple interpretations and not overtly programmatic, the work was inspired in part by events of the last few years in my native United States, and portions of the music might be heard as evoking by turns a sense of chaos and disorientation and a struggle between hope and despair. Some melodic material is derived from transcriptions of the rhythm and pitch contour of excerpts from recordings of public speeches delivered by Donald Trump and Barack Obama. The well-known American hymn “Amazing Grace” is also quoted in a fragmentary and/or disguised manner throughout the piece, with the most clearly recognizable statement heard near the end of the final movement.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.385 | 0.211 |
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".