From Voluptuous Pleasure to Voluptés: Delving into the Literary Translation Process
Bibliographic record
Abstract
As a literary translator, I am both a reader and a rewriter of the source texts. It is thus the very nature of my work to straddle the so-called “creative-critical divide.” This paper aims at giving a hands-on account of how the creative-critical divide impacts on my translation work. It focuses on my translation of three stories by Toronto author Marianne Apostolides where bodily/sexual tension was most present: “What We Do For Money,” “Coming of Age” and “Like a Cat.” Drawing from the principles of research-creation and genetic translation studies, I retrace my creative process from reading and interpreting to rewriting the texts in French by examining various drafts produced over several months, as well as verbalizing and categorizing my own feelings toward the original text and my translations.
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 | 0.033 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.066 |
| Scholarly communication | 0.032 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".