Bibliographic record
Abstract
This article describes and evaluates a co-authored translation of France Prešeren’s “Sonetni venec [A Wreath of Sonnets]” (1834), which has been translated once before into English, namely by Vivian de Sola Pinto in 1954. Having sketched the background to the undertaking, the author describes the structure of “Wreaths of Sonnets” in general and mentions some examples from English literature. He then places “Sonetni venec” in Prešeren’s oeuvre. Next, he exemplifies two other translations of “Wreaths” into English (one from Danish, the second from Czech). Most of the article is devoted to the strategy employed in this latest undertaking, a very complex and challenging one. Three particular problems had to be solved, given that the aim was to produce a translation that, formally, would be as close near feasible to the original. The first involved metre: Prešeren’s original was in iambic pentameters with feminine rhymes in line-finals; the translators chose to reject feminine rhymes, and their reasons are provided. Second, which particular frequently-recurrent rhymes would be chosen, given that one had to occur 24 times and another 18 times? This choice, also, is described. Third, Prešeren’s original has an acrostic in the Master Theme: we decided to attempt one in our version. The whole evaluation, which is some details compares this translation with the one by Pinto, is limited to an extremely formal framework.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".