Bibliographic record
Abstract
POUND'S CLASSICAL EDUCATION Ezra Pound was twelve when in 1897 he enrolled in the Cheltenham Military Academy near Wyncote, where he was given a solid grounding in English, Latin, Greek, history, and mathematics. We find a reference to the classical curriculum in a letter to his parents in June 1898, when he writes about his forthcoming holiday: “no more Latin, no more Greek / no more smoking on the sneak” – the conventional outburst of joy of the schoolboy who is (temporarily) freed of the tedium of many hours spent in learning grammar. At the Academy, both Greek and Latin were taught by Frederick James Doolittle, known among the cadets as “Cassius” because of his lean and hungry appearance. Pound later recalled in Guide to Kulchur that a man called Spenser recited a long passage from the Iliad to him, which “was worth more than grammar when one was 13 years old” ( GK , 145), while in Canto lxxx /532 we read that “old Spencer (,H.)…first declaimed me the Odyssey.” However, J. J. Wilhelm has shown that no existing bulletins of the school mention him, while his name does not appear in the census rolls of Wyncote and vicinity. When Pound looked back on his school years in “Early Translators of Homer” (1918), he observed that he probably was not “the sole creature who has been well taught his Latin and very ill-taught his Greek” ( LE , 249). The subsequent critical assumption that Pound was reasonably competent in Latin, but that his mastery of Greek was far from flawless, may have been strengthened by his own occasional overstated observations on the learning of the classics: “Really one don't need to know a language. One needs , damn well needs, to know the few hundred words in the few really good poems that any language has in it. It is better to know [Sappho's] poikilothron by heart than to be able to read Thucydides without trouble” ( SL , 93).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.025 |
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".