(Re)Configuring Language Identity and Memory in Eva Hoffman’s "Lost In Translation" / (Re)konfiguracija jezičkog identiteta i sećanja u romanu "Izgubljeno u prevodu" Eve Hofman
Bibliographic record
Abstract
The paper aims to examine Eva Hoffman’s experience of language and subsequent testimony of the trauma of immigration in her autobiographical novel Lost in Translation. As the daughter of Holocaust survivors, Eva Hoffman bears the burden of inherited memories of her parents’ Holocaust experiences, belonging to what Marianne Hirsch defines as the generation of postmemory. This status significantly impacts her sense of self and creates obstacles in the process of assimilation into a new country. Hoffman faces double immigration to Canada and the United States, where she struggles with her acquisition of the English language and finding an adequate narrative voice to testify to her family’s trauma and her own trauma of losing her Polish language and identity. To overcome the trauma of an unfamiliar space and language she initially feels disconnected from, Hoffman narrates her life and experiences in a new world. By examining the process of acceptance of a new language, readers witness Hoffman’s healing process and attempt to find closure in a world of fragmented, disassociated language and memories.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".