Bibliographic record
Abstract
INTRODUCTION For the past half-century, Pound's prolific and varied work has presented itself both as a rich landscape and as a perilous terrain for scholarly editors of his writing. There is a pressing need to establish correct and authoritative texts of Pound's poetry, translations, critical prose, musical compositions, reviews, letters, sound recordings, and assortments of archival material. Much of this material presents serious challenges to conventional notions of textual stability and authority, and in some cases confounds all attempts to harness it into published, readable forms. Despite such complexities, the general picture of Pound's influences, contacts, and working materials has come into sharper focus, allowing scholars of Pound's work to base their critical judgements upon increasingly more reliable texts. But this is not uniformly the case across Pound's writing. Texts in different genres present their own editorial problems and requirements, and questions of textual status and stability will range in urgency and extent across genres. At base, however, the editor's prime tasks remain: to present texts in optimally stable forms, or to account for why stability is not achievable; and to make plain the rationale and methods of the editing process, including (and perhaps especially) situations wherein no obvious alteration or correction has been made to a document beyond its presentation in publishable form.
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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".