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Record W4220839537 · doi:10.21226/ewjus707

Translating Ukrainian War Poetry into English: Why It Is Relevant

2022· article· en· W4220839537 on OpenAlexvenueno aff
Roman Ivashkiv

Bibliographic record

VenueEast/West Journal of Ukrainian Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEastern European Communism and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianPoetryLiteraturePoliticsSpanish Civil WarState (computer science)Translation studiesHistoryLinguisticsSociologyPolitical scienceArtPhilosophyLawComputer science

Abstract

fetched live from OpenAlex

This article explores the English translations of contemporary Ukrainian war poetry featured in the two anthologies Lysty z Ukrainy (Letters from Ukraine, 2016) and Words for War: New Poems from Ukraine (2017), through the prism of Jacques Derrida’s concept of “relevant.” It argues that although the economy of the original poems could not always be sustained, these translations nonetheless remain relevant primarily thanks to what they do rather than what they say. After contextualizing the recent (re)emergence of war poems as a genre of Ukrainian literature and providing an overview of the two translation anthologies, the article compares the Ukrainian originals with their English translations and discusses the various translation challenges. It then returns to Derrida’s own case study to extend the modifier “relevant” beyond its “economic” parameters to apply it more broadly to translation’s socio-political significance. It concludes with a discussion of how the two anthologies in question reflect the state of the reception of contemporary Ukrainian literature in the English-speaking world and how the translations they feature inform our understanding of the (un)translatability of poetry.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.015
Scholarly communication0.0100.008
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.061
GPT teacher head0.332
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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