Bibliographic record
Abstract
What is the publisher's role in the dissemination of translations, and how do they encourage readers to see the contextual links between history, politics, poetry, and translation? What dangers and advantages does the poet as translator face? Unlike the scholar-translator, the poet-translator is always a poet, with his own poetry at stake, and sometimes self-taught, with only a modest knowledge of the field entered. Will he impose his poetics on the text he is translating? Has he mastered the foreign language and literature, or should he turn to a translation team? Are graduate degree writing programs too dependent on English-language texts? Should students learn to read a second language well enough to take a translation workshop in that language? This article was originally presented as a paper on the panel ‘Poet as Translator: Promethean Risk’ at the 2003 Associated Writers Program Conference.
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 | 0.049 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.065 |
| Scholarly communication | 0.045 | 0.033 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 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".