<i>İmece</i> as Collaboration: Azra Erhat’s Collaborative Retranslation Projects
Bibliographic record
Abstract
This article situates collaborative (re)translation in the first decades of the Turkish Republic within a wider concept of imece (collective work), which sought to modernize the country in every field. İmece translations, produced primarily by the state-governed Translation Bureau (1940-1966), were wide-ranging endeavours that included the selection of texts, the creation of translations by several actants (who often contextualized the translations with paratexts), and the publishing of such translations. Within this context, the focus of the article is Azra Erhat, a distinguished translator, intellectual, and academic trained in this environment, where translation was not seen as a profession executed in solitude, but one that required the help and input of other agents as part of a broader cultural program. It explores two of her well-documented imece retranslations to shed light on these collaborative projects: Homer’s Iliad and Sappho’s poems, which she translated with two established poets, A. Kadir and Cengiz Bektaş, respectively. The article also discusses issues regarding subjectivity and retranslation as revealed in many of Erhat’s paratextual and extratextual statements, argues for a wider definition of subjectivity beyond the translation act, and establishes completeness, accuracy, and direct translation from the source text as the main motivations for retranslation.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".