Lost in Translation: The English Versions of Gabrielle Roy's Early Novels
Bibliographic record
Abstract
In translating the early works of Gabrielle Roy, Harry Binsse sought to make his English words sing the same song as the French source texts according to three principles: no omissions, no additions, no disfiguring flatness. Yet Binsse's very fidelity to these strictures led to substantive errors in the translations, altering characterization and meaning in Roy's novels. In avoiding flatness, Binsse's excessive lyricism and antiquated diction eclipsed Roy's signature simplicity. Conversely, his concern with linguistic and factual precision tended to mar any intended ambiguity or generalizations in the original text. Most significantly, Binsse's description of aboriginal and Third World peoples represents a different ideological perspective than Roy's, which the reader could mistakenly attribute to Roy. However, Binsse does ultimately adhere to his overall goal not to build barriers in translation, having widely contributed to the English accessibility and success of Roy's novels.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".