Bibliographic record
Abstract
INTRODUCTION: TOWARD THE PISAN CANTOS The Pisan Cantos , composed while Pound was incarcerated in the US Army Disciplinary Training Center (DTC) near Pisa in the summer of 1945, trail a long prehistory. In the late twenties and thirties Pound started preparing for the philosophical Paradiso with which he intended to conclude The Cantos . After 1939, though, while he was still assembling his materials, the war changed everything. Especially after the heavy Allied bombardment of Northern Italy during the last two years of World War II, Pound's focus turned from philosophy toward history. Lamenting the ruin of Italy's cultural patrimony, he composed a suite of poems in Italian beginning with what are now Cantos lxxii and lxxiii and including still unpublished texts that echo through the poem we know. Throughout the complex evolution of Pound's Italian and English compositions, his preoccupation with memory persists. Pound's philosophical preparations concern the way memory can reunite us with our divine beginnings. The wartime Italian writings stress the power of monuments to consolidate what is sometimes called collective or cultural memory. And in The Pisan Cantos themselves, Pound joins both to an urgent struggle to retain his deepest self, producing a suite whose lineaments his wife Dorothy recognized immediately upon receiving it in the post. These Cantos, she wrote him back, are “your self, the memories that make up yr. person” ( LC , 131).
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.152 | 0.059 |
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".