The Factor of Author's Reputation in Retranslations: Edgar Allan Poe on the Turkish Literary Scene
Bibliographic record
Abstract
This paper investigates the validity of André Lefevere’s assumption that “a canonized author is translated more on his own terms (according to his own poetics) than on those of the receiving system” (2000: 237) through a case study of Edgar Allan Poe retranslations in the Turkish literary system. The first part of the paper includes extratextual analysis carried out according to Gérard Genette’s categorization of “metatexts” and “paratexts,” and a further category which includes the social media. Poe’s poetics and the poetics of the Turkish literary system, as well as Poe’s reception in the system are explored through extratextual analysis to determine whether Poe gained more canonicity or reputation. The extratextual analysis reveals the author’s increasing influence, reception and reputation in the Turkish literary system over a time span of almost ninety years. The second part of the paper presents the textual analysis of Poe’s two tales, “Hop-Frog” and “The Masque of the Red Death”, in eight translations published between 1928 and 2002. Textual analysis serves to reveal whether Poe was translated more according to his own poetics as he became more reputable in the target literary system. The paper concludes that factors other than reputation of an author have also a role to play in translating an author according to his own poetics.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".