Ethics, Aesthetics and Décision: Literary Translating in the Wars of the Yugoslav Succession
Bibliographic record
Abstract
This is a participant-interpreter study of how issues of loyalty, ethics and ideology condition the action of a literary translator. A case-study is presented of the author’s socio-ethical dilemmas and decisions while translating Bosnian, Croatian and Serbian literature into English during the 1990s. This aims both to contribute to the socio-cultural historiography of that period and to illustrate how a literary translator might perform in settings of acute socio-cultural conflict. The case-study observations are then used to explore the nature of the literary translator as a textual and social actor. The “constrained autonomy” of the literary translator is seen as having several key implications. Among these are: that all translating acts have ethical and socio-political repercussions; that partiality informed by awareness of the demands of the wider social web may often be a more appropriate stance than neutrality; that the power structures within which the literary translator acts are more important than target language or translating strategy per se in determining source-culture representation, and that time/workload/chance factors may also play a role here; and that confronting Derrida’sindécidableis a defining feature of translator autonomy.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.026 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".