Criticizing the Turkish translation of the English poems in The Sun and Her Flowers by Rupi Kaur based on Dryden’s translation types
Bibliographic record
Abstract
The Canadian poet Rupi Kaur, a child of an immigrant family, is a rising figure in contemporary poetry which particularly focuses on immigration and womanhood. The English poems in the “rooting” chapter of her book The Sun and Her Flowers (2017a) reflect what she has experienced as a first-generation female immigrant. To analyze this experience in the target culture, this study concentrates on the poems translated into Turkish in Güneş ve Onun Çiçekleri (2017b) by Gizem Aldoğan in the "rooting" chapter. The study follows an eclectic method. The theoretical framework is based on John Dryden's three translation types (1992): Metaphrase, paraphrase and imitation. For the data analysis, the original and the translated poems are classified in terms of Hewson's macro-micro-macro methodological design (2011). Interrater reliability is ensured with the participation of three field experts during the data analysis. The macro-level analysis represents the final agreement of the field experts on the overall type of the translated poems in the defined chapter. The micro-level analysis, on the other hand, aims at finding out any unusual lines within a specific poem that fits into a translation type different from its macro-level type. The findings of the study show that the Turkish translations of Kaur's poems hold 100% paraphrastic translation style on the macro-level while there is a slight deviation on the micro-level.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".