Bibliographic record
Abstract
On March 1, 1944, upon receiving the Accademia Chigiana Quaderni dedicated to Vivaldi, Olga Rudge wrote to Count Chigi Saracini: “[Pound] looked envyingly at those elegant volumes! – his notes on the Malatesta manuscripts, and the Siena Cavalcanti, and the famous ‘Monte’ are always on his mind, but now it seems that his ‘Studi Sienesi’ will be published in…Venice!” Unfortunately, that plan was never realized, but Olga's letter gives us an idea of the high regard Pound had for the material that the Sienese sources provided for three separate projects. Pound's systematic use of archival documentation had begun in 1911 when his reading of the troubadours shifted from secondary to primary sources. His belief in the “resurrection” ( SL , 131) of lost details (for instance, Arnaut's melodies), together with his increasing need to test the accuracy of a printed text would in the long run shape his twofold personal philological techné , consisting of “paleography,” as with the 1932 Cavalcanti , for which he required reproductions of manuscripts “so as to show what we really do know and can know…How the stuff was first written down” ( EPS , 373); and then of recovering “the facts, original documents, etc.,” in order to prove “how loosely some history is written” ( EPS , 180). Consequently, the “‘aesthetic’ pleasure” he derived from the “‘unmediated’ experience with the documents produced by a culture that fascinated him” would never be entirely separate from the intellectual pleasure that the transmission of cultural data was capable of providing.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.513 | 0.256 |
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".