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Record W3174750010 · doi:10.4312/elope.18.1.125-137

Margaret Atwood’s Poetry in Slovene Translation

2021· article· en· W3174750010 on OpenAlexaboutno aff
Tjaša Mohar, Tomaž Onič

Bibliographic record

VenueELOPE English Language Overseas Perspectives and Enquiries · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryLiteratureArtHistory

Abstract

fetched live from OpenAlex

Margaret Atwood is undoubtedly the most popular Canadian author in Slovenia, with eight novels translated into Slovene. Although this prolific author also writes short fiction, poetry, children’s books, and non-fiction, these remain unknown to Slovene readers, at least in their own language. Atwood has published as many poetry collections as novels, but her poetry is inaccessible in Slovene, with the exception of some thirty poems that were translated and published in literary magazines between 1999 and 2009. The article provides an overview of Atwood’s poetry volumes and the main features of her poetry, as well as a detailed overview of Atwood’s poems that have appeared in Slovene translation, with the names of translators, titles of poetry collections, dates of publication, and names of literary magazines. This is the first such overview of Slovene translations of Atwood’s poetry. Additionally, the article offers an insight into some stylistic aspects of Atwood’s poetry that have proven to be particularly challenging for translation.

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)0.001
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.975
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.004
Scholarly communication0.0060.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.010
GPT teacher head0.232
Teacher spread0.221 · 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
GenreOther

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

Citations1
Published2021
Admission routes1
Has abstractyes

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