Analyzing, transmitting, and editing an Anatolian tale: A literary translation project as process
Bibliographic record
Abstract
This study is based on a literary translation project conducted within the framework of a four-year translation and interpretation undergraduate program. A literary translation course is offered in the sixth term of the program as part of the literature module. In the first stage of the course, students read and analyze literary works using operations of analysis and compare them with their translations to determine meaning transformations of any type. This process is expected to enable students to develop an awareness of the indispensability of textual analysis for translation and the possibility of different kinds of meaning transformations. With this dual awareness, students as prospective translators are expected to be able to better understand and transfer the signs that constitute a literary work and avoid unintended meaning transformations. In the second stage, students choose a short story to translate by applying the knowledge and skills they acquired in the first stage. After this process, they edit the translated text. At the end of the semester, to share their translation with their class, students prepare a presentation on their analysis of the source text as well as the translation and editing processes, emphasizing the notable outcomes of the analysis and related decisions made to transfer distinctive signs. In this study, the translation of İki Peri Kızı (The Fairy Sisters) by Tahsin Yücel is aimed at providing an example for literary translation projects that may be conducted in similar contexts.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".