Pizarnik through Levine’s Looking Glass: How Subversive Is the Scribe?1
Bibliographic record
Abstract
Suzanne Jill Levine is known above all for her English translations of Cabrera Infante, Sarduy and Puig, with whom she worked closely. While a lot has been written about her translations of fiction by men, little research has been done on her translations of women writers. In this paper, I analyse a selection of her English renditions of Alejandra Pizarnik in order to see how Levine behaves when translating the poetic work of a woman. First, considering that Levine describes herself as a “subversive scribe, ‘transcreating’ writing that stretches the boundaries of patriarchal discourse,” how does the fact that she shares the author’s gender affect her “transcreations?” Then, bearing in mind that Levine has often stressed the complexity of fiction translation, refuting the “common belief that novels are easier to translate than poetry,” how does she deal with the translation of lyrical poems? And last, how rebellious is she when translating an author who has passed away and whom she cannot consult?
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".