LUCY MAUD MONTGOMERY’NİN “YEŞİL’İN KIZI” ADLI KİTABININ YAZIN ÇEVİRİSİ VE MAKİNE ÇEVİRİSİ ÇERÇEVESİNDE KARŞILAŞTIRMALI İNCELENMESİ
Bibliographic record
Abstract
This study includes a discussion on the literary translation and machine translation of “Anne of Green Gables”, a novel by the Canadian writer Lucy Maud Montgomery, one of the popular culture publications, the novel is a summary of the events that are narrated in what is originally a collection of eight books. In this article, differences between human and machine translation have been studied, with special emphasis on the importance of human factor in translation. This study has been conducted by using online machine translation applications such as Google and Yandex translation tools. The article aims to highlight the importance of features peculiar to the writer and the work at the stage of post-editing done by a human translator. Besides, this study tries to find out the best way to make translations of literary works in recent technological research on machine translation. An assessment has been made on the translation of the novel by conventional methods and by applications of computer-aided machine translations. One of the goals of this study is to show that, as with many types of publications, popular culture publications can be rendered through machine translation. In the evaluation, the method of “rule-based machine translation” has been preferred. This method, which allows the researcher to work more actively, has been found to be more scientific than other types of machine translation. Moreover, it has been found that machine translation systems such as Google Translate and Yandex Translate have a number of shortcomings in transferring concepts related to the sense being communicated, and that they operate by a system based on structures at lexical level. Today, with the machine translation becoming more widely available, it has been seen that post-editing done by human editors contributes significantly to enhancing the quality of literary translations, including works of popular culture. In this process, special emphasis has been placed on issues such as post-editors’ conception of the task being done, their way of looking at the world, and their knowledge about literary translation. The examples given in this study revealed that, particularly in literary translation, every post-editing procedure does not always yield effective results, and that everything depends on the translator’s background knowledge and his/her competence in literary translation. Discussions have been made on this topic, with special emphasis on the translator’s ability to interpret a literary text.
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 | 0.001 | 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 | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".