Bibliographic record
Abstract
In February of 1952, from his room in St. Elizabeths mental hospital, Ezra Pound wrote to his old friend Olivia Rossetti Agresti – niece of poet Dante Gabriel Rossetti, one-time editor of the fin-de-siècle anarchist journal The Torch , and latterly – like Pound – an ardent supporter of Mussolini. But neither anarchism nor fascism was on the table at this moment; the question was race and culture: Eropewns will nebber understan’ deh Kull ud race / marse blackman will most certainly not return to Africa to infect what the dirty brits have left there / with any more occidental hogwash / He will stay here…being human and refusing to be poured into a mould and cut to the stinckging patter[n] on the slicks and the weakly papers. An occasional upsurge of African agricultural heritage as in G. W. Carver, o.kay but also marse Blakman him lazy / lazy as Lin Yu Tang. thank god for it / as a humanizing element most needed here / tho yr beloved kikes try to utilize him for purposes of demoralization / hell / he ain’ nebber been moralized / thank God. Since the mid 1980s, scholars have been combing Pound's voluminous correspondence for this kind of pronouncement and have not surprisingly discovered the racism they expected. Indeed, Pound proves to have been as obsessed with race as everyone else in America.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.010 |
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".