Translation Between Domestication and Foreignization: The Perspective of the Canadian Novelist Anne Michaels
Bibliographic record
Abstract
This paper explores the interplay between domestication and foreignization in Anne Michaels’ novel Fugitive Pieces, illustrating how translation functions as both a means of adaptation and a tool for preserving identity. Domestication, which involves aligning a translated text with the target culture, is depicted through the protagonist Jakob Beer’s survival strategies. Escaping the Nazis and integrating into Greek and English-speaking societies, Jakob learns new languages, reflecting the necessity of cultural assimilation for survival. However, this process also distances him from his original identity, illustrating the cost of domestication. Conversely, foreignization, which retains the original cultural essence of a text, manifests in Jakob’s persistent connection to his Jewish heritage and past traumas. Despite acquiring new languages, Jakob remains tied to his lost world, demonstrating how translation can serve as an act of remembrance rather than mere linguistic substitution. Michaels presents translation as a negotiation between freedom and constraint, where linguistic choices reflect deeper existential struggles. Through this analysis, Fugitive Pieces emerges as a meditation on the challenges of translation—both linguistic and existential—highlighting how survivors and translators alike navigate the delicate balance between adaptation and preservation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.052 | 0.044 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".