Feminist translation practices in Turkey: The case of the feminist websites 5Harfliler and Çatlak Zemin
Bibliographic record
Abstract
Feminist Translation Studies (FTS), which examines the interaction between gender and translation emerged in bilingual Quebec in Canada in the late 1970s and early 1980s coinciding with the cultural turn which brought the concept of ideology to the centre of translation studies. Since then, many studies have been conducted within the scope of FTS and they have generally focused on the translation of printed literary texts. However, in recent years, feminist translation scholars have begun to criticize this restrictive tendency in FTS and to call for more studies on the translation of non-literary texts. Paying attention to this call and keeping up with the digital transformation of feminism, this study focuses on the translations on two Turkish feminist websites and tries to shed new light on feminist translation practices in Turkey. This study aims to explore the ways and the extent to which two Turkish feminist websites, 5Harfliler and Çatlak Zemin, are engaged in feminist translation practices. To this end, thematic and paratextual analyses were performed on the articles translated from English into Turkish and published on 5Harfliler and Çatlak Zemin from their establishment until the end of 2018. The results of the analyses showed that both 5Harfliler and Çatlak Zemin performed feminist translation practices, albeit to varying degrees, thanks to the selection of source texts addressing feminist themes and the usage of paratextual feminist translation strategies, i.e., prefaces and footnotes which make women and translators visible.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".