Slovak poetry in English translation after the collapse of state socialism: Tracing the trajectories of internationalisation
Bibliographic record
Abstract
Moving literary texts from a peripheral language to a hyper-central one (Heilbron 1999) goes against the general flow of translations, and agents involved in this process play crucial roles in it. In this article, on the case of English translations of Slovak poetry, I set out to investigate how such processes work. My research starts with assembling a bibliography of English translations of Slovak poetry published in book form between 1989 and 2020. The list contains no fewer than 2,500 poems by 161 poets, translated by more than 50 translators. A few observations from the quantitative analysis I conducted help answer such questions as what kind of agents translate poetry in these projects, who gets translated and how likely it is that the volumes reach an international readership. Subsequently, I use tools from Bourdieu’s field theory and Latour’s actor-network theory (ANT) to trace actor-networks pertaining to those translation projects concerning the rendering of two chosen Slovak poets who hold different positions in the Slovak literary field—Mila Haugová (born in 1942) and Milan Richter (born in 1948).
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".