Rethinking the Model of Translation of the Literary Text as Creative Interpretation and Examining the Translation of the Literary Title as a Problem of “Hidden Meanings”
Bibliographic record
Abstract
This paper argues against the inclusion of the translation of works of literature in the commonly theorized model of semantic transferal from the source text to the target text and for the role of the title in the complex structure of the literary work of art. Works of literature – including their titles – are by their nature polysemic. One of the terminological deficiencies of English literary scholarship is the lack of a term for the “hidden meanings” of a literary text. Unfortunately, the use of the word “sense” by Frank Kermode has not become accepted, nor has the key role of the title and also the ending of a work been sufficiently recognized in English literary scholarship. The function of the title in creating “hidden meanings” resists generalization, yet the issue of translating the title has not received deserved attention, although it is central to the work of analysis of the text that the translator must undertake.
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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.078 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
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".