Multiple im/person/aliz/ations: Four Attempts to 'get under the skin' of Poets
Bibliographic record
Abstract
I have been actively translating for about twenty years. Looking back, I now realize that it made translation easier when I tried to ‘become’ the original writer: I was more successful when I asked myself, “what would they have written if they had had my knowledge of English?” and, for poetry, when faced with the clash between the demands of form and content, “which way would they bend?’ Rather than attempt any theorizing, I propose to relate my efforts to get under the skin of a number of poets, for example: one, surviving the siege of Leningrad; another, pioneering multiple poetic genres in early 19th-century Central Europe; a third who (successfully? I am not sure) aimed to capture the horror of a Nazi atrocity in Vienna; a fourth who became the most popular author of Slovene poetry for children by temporarily shedding his own adulthood. Also, I will add my recent attempts to capture, in Slovene, the style of children in war-torn Northern Uganda who are writing to the sponsors who are paying their school fees in a charming but not always clear fractured English (which they are just learning): is it possible, is it expedient to pretend to be such a child in order to transfer their thoughts into Slovene? It certainly helps to have been a teacher. Teachers are, I believe, better teachers if they can act the roles of others, and translators can perhaps be better translators if they can ‘become’ other people. Anyway, it makes for a more interesting life.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.036 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".