A landscape of shifting identities amid urban invasion: Tamara Duda’s novel Daughter through a translation lens
Bibliographic record
Abstract
In the environment of Russia’s ongoing war against Ukraine, literary translation acquires critical significance as a way to get Ukraine’s narratives of destruction and urbicide across cultural and political borders. This article will focus on Daisy Gibbons’s 2021 translation of Tamara Duda’s 2019 novel Daughter, set in the Eastern Ukrainian city of Donetsk, to examine the translator’s project of reconstructing the complex interplay of Eastern and Western Ukrainian identities embroiled in the narrative of crawling occupation. Daughter tells the story of Russia’s 2014 invasion of Donetsk, dissecting the city’s fragmented identity along cultural and linguistic divides and exploring internal tensions and propaganda-fueled conflicts leading to its eventual downfall. The storyline adopts the female protagonist’s insider/outsider perspective, tracing her gradual evolution from an invisible observer to a fearless insurgent fighting for the survival of her unravelling home. The analysis will centre on the translator’s approach, which combines textual and paratextual techniques to highlight the processes of division and destruction – with their transformative impact on the urban space – and to enter into a visible dialogue with the narrator/protagonist’s voice to amplify and reinforce its distinctly pro-Ukrainian perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".