Bibliographic record
Abstract
In the present paper we shall focus on literary translation, on the question ofthe translation of “complex” or “secondary” texts, but with the intention of makinga contribution to the problem of translating non-literary, “simple,” “primary” textsas well. In other words, we shall examine the problem of text translation from asemiotic perspective. In fact, this study founds translation theory in sign theorydeveloping a semiotic-linguistic approach to the problem of translation in thedirection of so-called interpretation semiotics which also implies the semiotics ofsignificance. Translation concerns both simple and complex texts, which correspondrespectively to Mikhail Bakhtin’s primary and secondary texts. Simple texts concernnon literary discourse genres whilst complex texts the literary genres, where theformer are better understood in the light of the latter, and not vice versa. Thispaper also focuses on the concept of the literary text as a hypertext maintainingthat the hypertext is a methodics for translative practice. The relation between thetextandlanguageunderstood as a modeling device is also important for anadequate theory of translation and sheds light on the question of translatability.Another central issue in this study is the relation between translation andintertextuality.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".