Bibliographic record
Abstract
Pound, in the teens and 1920s, understood the literary logic of modernism, with its poetics of difficulty and allusiveness, more clearly than any of his contemporaries. He pushed his insights further, into an extreme, enormous, all-but-unreadable book – the “Cantos”…. By the age of fifteen Ezra Pound “already knew, apparently, pretty much what he wanted to do – that by the time he was 30 he ‘would know more about poetry than any man living’” (Moody, 13). He first read Browning's Sordello in 1904. “I began to get it on about the 6th reading,” he would write to his father, and he thought he might be able to model his own long poem on Browning's difficult epic about the Italian adventurer who became a troubadour. Within a year he found a way to study Provençal. In spring term of 1905, the last trimester of his senior year at Hamilton College, he was introduced to the language by Professor William P. Shepard, cementing a relationship that would last as long as Shepard lived. Pound and Bill, as Pound called him, read H. J. Chaytor's Troubadours of Dante , which included poems by Sordello as well as Bertran de Born, Arnaut Daniel, and others whom Pound would continue to read for years. In May the Hamilton Literary Magazine published his version of a text in Latin and archaic Provençal that he called the “Belangal Alba.” After graduating from Hamilton, Pound returned to the University of Pennsylvania (where he had spent his freshman and sophomore years), near his family's home in the Philadelphia suburb of Wyncote. In 1905–6, as a student in the Master's program in Romanic Languages, he studied almost exclusively with the Hispanist Hugo Rennert, working on Spanish (Old Spanish, Spanish Drama, Spanish Literature), Old French, Italian (Petrarch), and more Provençal. He bought a copy of Carl Appel's Provenzalische Chrestomathie (Provençal Anthology) in the second edition (1902) and dated it 1905, presumably in the fall when he began his work with Rennert.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".