Bibliographic record
Abstract
From Issue Editor Özet Nesir: Edebiyat Çalışmaları Dergisi’nin yedinci sayısı ilhamını, Else Vieira’nın 1990’ların ortasında çeviri çalışmaları kapsamında öne sürdüğü, çeviri ve diğer yorumlayıcı süreçlerde kurmacanın önemini vurgulayan “kurgusal dönüş” (fictional turn) tanımından alıyor. Vieira’nın, Brezilyalı ve Latin Amerikalı yazarların eserlerinde bulunan çeviriyle ilgili temalar üzerine yaptığı çalışma, Edwin Gentzler ve Rosemary Arrojo gibi diğer akademisyenler tarafından daha da geliştirilmiştir. Arrojo, Fictional Translators: Rethinking Translation Through Literature (2018) adlı eserinde, çeviri çalışmalarının disiplinlerarası bir alan olarak büyüdüğünü ve çevirmen karakterlerin 2000li yılların başlarında dünya edebiyatında daha sık görünür olduğunu, kurmacada çevirmenlere ve çeviriyle ilgili temalara olan akademik ilginin “başka yerlerde de [Latin Amerika dışında] eleştirel bir malzeme bolluğu üretmeye başladığını” gözlemlemektedir. Nesir’in bu sayısında yer alan makaleler, Türk edebiyatında çeviri edimine ve çevirmen karakterlere olan akademik ilginin arttığını kanıtlayan araştırmalar içermektedir. Abstract The special dossier of the seventh issue of Nesir: Journal of Literary Studies draws inspiration from the “fictional turn” in translation studies proposed by Else Vieira in the mid-1990s to highlight the value of fiction as a credible source for exploring and reflecting on translation and other interpretive processes. Vieira’s work on the role of translation-related themes in the fiction of Brazilian and Latin American authors was further developed by other scholars, including Edwin Gentzler and Rosemary Arrojo. In her Fictional Translators: Rethinking Translation Through Literature (2018), Arrojo observes that as translation studies grew as an interdisciplinary field and translator characters became more prominent in world literature around the turn of the millennium, academic interest in the portrayal of translators and translation-related themes in fiction “began to produce an abundance of critical material elsewhere as well.” The articles featured in Nesir’s current issue are a testament to the growing body of scholarship that explores translator characters from various literary traditions, with a particular focus on Turkish literature in this case.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.370 | 0.205 |
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".