Bibliographic record
Abstract
American composer James Tenney produced a wide range of innovative works, including computer music, Fluxus-inspired text scores, and chance-based instrumental pieces founded on the overtone series. Tenney’s music is characterized by a fascination with sound and how listeners perceive it. In addition to his creative work, Tenney is the author of important theoretical writings on the psychology and phenomenology of musical experience. Like John Cage, Tenney intentionally avoids rhetorical gestures in his music, following his dictum that "[T]he focus should be on the sound itself and not on the ideas and emotions of the composer" (Tenney, 2005). Tenney was born in Silver City, New Mexico, but moved to New York in the 1950s to study piano with Eduard Steuermann and composition with Chou-Wen Chung. Later studies at Bennington College and the University of Illinois brought him into contact with Carl Ruggles, Lionel Nowak, Kenneth Gaburo, and Lejaren Hiller. In works from this period, such as Seeds (1956–61), composer Larry Polansky identifies the strong influence of Anton Webern and Edgard Varèse, two of Tenney’s early inspirations.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".