Bibliographic record
Abstract
A & C Black's 1929 Who's Who carried Ezra Pound's entry as “Poet and composer.” The music consisted of Le Testament (in three versions) – an opera on François Villon's poem of the same name, and ten violin pieces, including a setting of “Sestina Altaforte.” A first essay on the relationship of poetry to music (“I Gather the Limbs of Osiris”) eventually led to a weekly column as music critic for The New Age . Pound's sustained training of his ear gradually brought him to composing from this foundation in criticism. The sequence was, of course, backwards: composition came last. Pound fought a hidden war against the “tyranny of words” and his own musical ignorance. Though he played elementary piano, his background hardly prepared him for the outré reaches of music theory and notation he undertook. Trained as a medievalist, Pound taught himself to read troubadour music, transpose cantus firmus , and transcribe the medieval neums into Western notation. In a modern idiom he studied overtones in harmonization, customized “melodic” minor scales to his needs, and developed composer's shorthand for polyrhythms and ostinati. We shouldn't be distracted by reports of a crude singing voice or tone-deafness. Neither presented an obstacle to composition or theory. Music, in short, presented rough terrain. Ironically, Pound’s ur-text for composition was a seminal work of literary criticism: Dante’s De vulgarii eloquentia . Dante advocated armonia in the words and spelled out, with examples, the methods to achieve it. Returning to this work for a refresher on the arrangement of syllables, the application of dissonance, the delight in experiment, we gain entrée to Pound’s music.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.018 |
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".